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henlopen conference | https://en.wikipedia.org/wiki/Henlopen_Conference | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13054553-12.html.csv | count | two teams in the henlopen conference ended with a division record of 4-2 . | {'scope': 'all', 'criterion': 'equal', 'value': '4-2', 'result': '2', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'division record', '4-2'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose division record record fuzzily matches to 4-2 .', 'tostr': 'filter_eq { all_rows ; division record ; 4-2 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; division record ; 4-2 } }', 'tointer': 'select the rows whose division record record fuzzily matches to 4-2 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; division record ; 4-2 } } ; 2 } = true', 'tointer': 'select the rows whose division record record fuzzily matches to 4-2 . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; division record ; 4-2 } } ; 2 } = true | select the rows whose division record record fuzzily matches to 4-2 . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'division record_5': 5, '4-2_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'division record_5': 'division record', '4-2_6': '4-2', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'division record_5': [0], '4-2_6': [0], '2_7': [2]} | ['school', 'team', 'division record', 'overall record', 'season outcome'] | [['milford', 'buccaneers', '5 - 1', '10 - 2', 'won div ii state championship'], ['laurel', 'bulldogs', '5 - 1', '9 - 3', 'loss in div ii state championship game'], ['indian river', 'indians', '4 - 2', '7 - 4', 'loss in first round of div ii playoffs'], ['delmar', 'wildcats', '4 - 2', '8 - 2', 'failed to make playoffs'], ['seaford', 'blue jays', '2 - 4', '2 - 8', 'failed to make playoffs'], ['lake forest', 'spartans', '1 - 5', '3 - 7', 'failed to make playoffs'], ['woodbridge', 'blue raiders', '0 - 6', '0 - 10', 'failed to make playoffs']] |
werner pfirter | https://en.wikipedia.org/wiki/Werner_Pfirter | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16431762-2.html.csv | majority | in all of the years , the number of wins was zero . | {'scope': 'all', 'col': '5', 'most_or_all': 'all', 'criterion': 'equal', 'value': '0', 'subset': None} | {'func': 'all_eq', 'args': ['all_rows', 'wins', '0'], 'result': True, 'ind': 0, 'tointer': 'for the wins records of all rows , all of them are equal to 0 .', 'tostr': 'all_eq { all_rows ; wins ; 0 } = true'} | all_eq { all_rows ; wins ; 0 } = true | for the wins records of all rows , all of them are equal to 0 . | 1 | 1 | {'all_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'wins_3': 3, '0_4': 4} | {'all_eq_0': 'all_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'wins_3': 'wins', '0_4': '0'} | {'all_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'wins_3': [0], '0_4': [0]} | ['year', 'class', 'team', 'points', 'wins'] | [['1970', '250cc', 'yamaha', '0', '0'], ['1970', '350cc', 'yamaha', '0', '0'], ['1971', '250cc', 'yamaha', '9', '0'], ['1971', '350cc', 'yamaha', '33', '0'], ['1972', '250cc', 'yamaha', '28', '0'], ['1972', '350cc', 'yamaha', '17', '0'], ['1973', '250cc', 'yamaha', '20', '0'], ['1973', '350cc', 'yamaha', '17', '0']] |
1999 senior pga tour | https://en.wikipedia.org/wiki/1999_Senior_PGA_Tour | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11621747-4.html.csv | aggregation | in the 1999 senior pga tour , the average earnings of the top five ranked golfers was $ 8,661,168.40 . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '$ 8,661,168.40', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'earnings'], 'result': '$ 8,661,168.40', 'ind': 0, 'tostr': 'avg { all_rows ; earnings }'}, '$ 8,661,168.40'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; earnings } ; $ 8,661,168.40 } = true', 'tointer': 'the average of the earnings record of all rows is $ 8,661,168.40 .'} | round_eq { avg { all_rows ; earnings } ; $ 8,661,168.40 } = true | the average of the earnings record of all rows is $ 8,661,168.40 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'earnings_4': 4, '$8,661,168.40_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'earnings_4': 'earnings', '$8,661,168.40_5': '$ 8,661,168.40'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'earnings_4': [0], '$8,661,168.40_5': [1]} | ['rank', 'player', 'country', 'earnings', 'wins'] | [['1', 'hale irwin', 'united states', '9645485', '25'], ['2', 'jim colbert', 'united states', '8887831', '19'], ['3', 'lee trevino', 'united states', '8666030', '28'], ['4', 'dave stockton', 'united states', '8104786', '14'], ['5', 'bob charles', 'new zealand', '8001710', '23']] |
1963 vfl season | https://en.wikipedia.org/wiki/1963_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10783853-7.html.csv | unique | the only game with fewer than 17000 spectators was played at punt road oval . | {'scope': 'all', 'row': '5', 'col': '6', 'col_other': '5', 'criterion': 'less_than', 'value': '17000', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'crowd', '17000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose crowd record is less than 17000 .', 'tostr': 'filter_less { all_rows ; crowd ; 17000 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_less { all_rows ; crowd ; 17000 } }', 'tointer': 'select the rows whose crowd record is less than 17000 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'crowd', '17000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose crowd record is less than 17000 .', 'tostr': 'filter_less { all_rows ; crowd ; 17000 }'}, 'venue'], 'result': 'punt road oval', 'ind': 2, 'tostr': 'hop { filter_less { all_rows ; crowd ; 17000 } ; venue }'}, 'punt road oval'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_less { all_rows ; crowd ; 17000 } ; venue } ; punt road oval }', 'tointer': 'the venue record of this unqiue row is punt road oval .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_less { all_rows ; crowd ; 17000 } } ; eq { hop { filter_less { all_rows ; crowd ; 17000 } ; venue } ; punt road oval } } = true', 'tointer': 'select the rows whose crowd record is less than 17000 . there is only one such row in the table . the venue record of this unqiue row is punt road oval .'} | and { only { filter_less { all_rows ; crowd ; 17000 } } ; eq { hop { filter_less { all_rows ; crowd ; 17000 } ; venue } ; punt road oval } } = true | select the rows whose crowd record is less than 17000 . there is only one such row in the table . the venue record of this unqiue row is punt road oval . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_less_0': 0, 'all_rows_6': 6, 'crowd_7': 7, '17000_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'venue_9': 9, 'punt road oval_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_less_0': 'filter_less', 'all_rows_6': 'all_rows', 'crowd_7': 'crowd', '17000_8': '17000', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'venue_9': 'venue', 'punt road oval_10': 'punt road oval'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_less_0': [1, 2], 'all_rows_6': [0], 'crowd_7': [0], '17000_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'venue_9': [2], 'punt road oval_10': [3]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['north melbourne', '8.10 ( 58 )', 'st kilda', '9.12 ( 66 )', 'arden street oval', '17125', '1 june 1963'], ['geelong', '9.12 ( 66 )', 'hawthorn', '9.12 ( 66 )', 'kardinia park', '29374', '1 june 1963'], ['collingwood', '10.11 ( 71 )', 'essendon', '13.9 ( 87 )', 'victoria park', '44501', '1 june 1963'], ['south melbourne', '11.8 ( 74 )', 'melbourne', '8.22 ( 70 )', 'lake oval', '17160', '1 june 1963'], ['richmond', '17.13 ( 115 )', 'fitzroy', '13.8 ( 86 )', 'punt road oval', '16500', '1 june 1963'], ['footscray', '7.7 ( 49 )', 'carlton', '8.9 ( 57 )', 'western oval', '26107', '1 june 1963']] |
michel rougerie | https://en.wikipedia.org/wiki/Michel_Rougerie | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14889717-2.html.csv | comparative | for the races , aermacchi played in earlier years than harley davidson . | {'row_1': '2', 'row_2': '3', 'col': '1', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'aermacchi'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team record fuzzily matches to aermacchi .', 'tostr': 'filter_eq { all_rows ; team ; aermacchi }'}, 'year'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; team ; aermacchi } ; year }', 'tointer': 'select the rows whose team record fuzzily matches to aermacchi . take the year record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'harley davidson'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose team record fuzzily matches to harley davidson .', 'tostr': 'filter_eq { all_rows ; team ; harley davidson }'}, 'year'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; team ; harley davidson } ; year }', 'tointer': 'select the rows whose team record fuzzily matches to harley davidson . take the year record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; team ; aermacchi } ; year } ; hop { filter_eq { all_rows ; team ; harley davidson } ; year } } = true', 'tointer': 'select the rows whose team record fuzzily matches to aermacchi . take the year record of this row . select the rows whose team record fuzzily matches to harley davidson . take the year record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; team ; aermacchi } ; year } ; hop { filter_eq { all_rows ; team ; harley davidson } ; year } } = true | select the rows whose team record fuzzily matches to aermacchi . take the year record of this row . select the rows whose team record fuzzily matches to harley davidson . take the year record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'team_7': 7, 'aermacchi_8': 8, 'year_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'team_11': 11, 'harley davidson_12': 12, 'year_13': 13} | {'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'team_7': 'team', 'aermacchi_8': 'aermacchi', 'year_9': 'year', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'team_11': 'team', 'harley davidson_12': 'harley davidson', 'year_13': 'year'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'team_7': [0], 'aermacchi_8': [0], 'year_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'team_11': [1], 'harley davidson_12': [1], 'year_13': [3]} | ['year', 'class', 'team', 'points', 'rank', 'wins'] | [['1972', '125cc', 'aermacchi', '2', '38th', '0'], ['1972', '350cc', 'aermacchi', '3', '30th', '0'], ['1973', '250cc', 'harley davidson', '45', '5th', '0'], ['1973', '350cc', 'harley davidson', '4', '34th', '0'], ['1973', '500cc', 'harley davidson', '6', '28th', '0'], ['1974', '250cc', 'harley davidson', '21', '9th', '0'], ['1974', '350cc', 'harley davidson', '25', '7th', '0'], ['1974', '500cc', 'harley davidson', '14', '16th', '0'], ['1975', '250cc', 'harley davidson', '76', '2nd', '2'], ['1975', '500cc', 'harley davidson', '4', '28th', '0'], ['1976', '500cc', 'suzuki', '16', '14th', '0'], ['1977', '250cc', 'yamaha', '5', '27th', '0'], ['1977', '350cc', 'yamaha', '50', '4th', '1'], ['1977', '500cc', 'suzuki', '21', '13th', '0'], ['1978', '350cc', 'yamaha', '47', '6th', '0'], ['1978', '500cc', 'suzuki', '23', '10th', '0'], ['1979', '350cc', 'yamaha', '10', '17th', '0'], ['1979', '500cc', 'suzuki', '16', '15th', '0'], ['1980', '500cc', 'suzuki', '4', '17th', '0'], ['1981', '350cc', 'yamaha', '1', '32nd', '0']] |
77th united states congress | https://en.wikipedia.org/wiki/77th_United_States_Congress | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1958768-3.html.csv | unique | the only vacator in the 77th . us congress to resign for entry into the us army is charles l. faddis . | {'scope': 'all', 'row': '15', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'to enter the us army', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'reason for change', 'to enter the us army'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose reason for change record fuzzily matches to to enter the us army .', 'tostr': 'filter_eq { all_rows ; reason for change ; to enter the us army }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; reason for change ; to enter the us army } }', 'tointer': 'select the rows whose reason for change record fuzzily matches to to enter the us army . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'reason for change', 'to enter the us army'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose reason for change record fuzzily matches to to enter the us army .', 'tostr': 'filter_eq { all_rows ; reason for change ; to enter the us army }'}, 'vacator'], 'result': 'charles i faddis ( d )', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; reason for change ; to enter the us army } ; vacator }'}, 'charles i faddis ( d )'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; reason for change ; to enter the us army } ; vacator } ; charles i faddis ( d ) }', 'tointer': 'the vacator record of this unqiue row is charles i faddis ( d ) .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; reason for change ; to enter the us army } } ; eq { hop { filter_eq { all_rows ; reason for change ; to enter the us army } ; vacator } ; charles i faddis ( d ) } } = true', 'tointer': 'select the rows whose reason for change record fuzzily matches to to enter the us army . there is only one such row in the table . the vacator record of this unqiue row is charles i faddis ( d ) .'} | and { only { filter_eq { all_rows ; reason for change ; to enter the us army } } ; eq { hop { filter_eq { all_rows ; reason for change ; to enter the us army } ; vacator } ; charles i faddis ( d ) } } = true | select the rows whose reason for change record fuzzily matches to to enter the us army . there is only one such row in the table . the vacator record of this unqiue row is charles i faddis ( d ) . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'reason for change_7': 7, 'to enter the us army_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'vacator_9': 9, 'charles i faddis (d)_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'reason for change_7': 'reason for change', 'to enter the us army_8': 'to enter the us army', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'vacator_9': 'vacator', 'charles i faddis (d)_10': 'charles i faddis ( d )'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'reason for change_7': [0], 'to enter the us army_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'vacator_9': [2], 'charles i faddis (d)_10': [3]} | ['district', 'vacator', 'reason for change', 'successor', 'date successor seated'] | [['oklahoma 7th', 'sam c massingale ( d )', 'died january 17 , 1941', 'victor wickersham ( d )', 'april 1 , 1941'], ['new york 17th', 'kenneth f simpson ( r )', 'died january 25 , 1941', 'joseph c baldwin ( r )', 'march 11 , 1941'], ['alabama 7th', 'walter w bankhead ( d )', 'resigned february 1 , 1941', 'carter manasco ( d )', 'june 24 , 1941'], ['maryland 6th', 'william d byron ( d )', 'died february 27 , 1941', 'katharine byron ( d )', 'may 27 , 1941'], ['new york 42nd', 'pius l schwert ( d )', 'died march 11 , 1941', 'john c butler ( r )', 'april 22 , 1941'], ['north carolina 5th', 'alonzo d folger ( d )', 'died april 30 , 1941', 'john h folger ( d )', 'june 14 , 1941'], ['new york 14th', 'morris m edelstein ( d )', 'died june 4 , 1941', 'arthur g klein ( d )', 'july 29 , 1941'], ['wisconsin 1st', 'stephen bolles ( r )', 'died july 8 , 1941', 'lawrence h smith ( r )', 'august 29 , 1941'], ['pennsylvania 15th', 'albert g rutherford ( r )', 'died august 10 , 1941', 'wilson d gillette ( r )', 'november 4 , 1941'], ['colorado 4th', 'edward t taylor ( d )', 'died september 3 , 1941', 'robert f rockwell ( r )', 'december 9 , 1941'], ['california 17th', 'lee e geyer ( d )', 'died october 11 , 1941', 'cecil r king ( d )', 'august 25 , 1942'], ['massachusetts 7th', 'lawrence j connery ( d )', 'died october 19 , 1941', 'thomas j lane ( d )', 'december 30 , 1941'], ['pennsylvania 11th', 'patrick j boland ( d )', 'died may 18 , 1942', 'veronica g boland ( d )', 'november 3 , 1942'], ['california 3rd', 'frank h buck ( d )', 'died september 17 , 1942', 'vacant until the next congress', 'vacant until the next congress'], ['pennsylvania 25th', 'charles i faddis ( d )', 'resigned december 4 , 1942 to enter the us army', 'vacant until the next congress', 'vacant until the next congress'], ['illinois 6th', 'a f maciejewski ( d )', 'resigned december 6 , 1942', 'vacant until the next congress', 'vacant until the next congress']] |
lee gibson | https://en.wikipedia.org/wiki/Lee_Gibson | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17624963-2.html.csv | unique | lee gibson 's fight against muhsin corbbrey was the only time that he fought in california . | {'scope': 'all', 'row': '2', 'col': '7', 'col_other': '3', 'criterion': 'equal', 'value': 'california , united states', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'california , united states'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record fuzzily matches to california , united states .', 'tostr': 'filter_eq { all_rows ; location ; california , united states }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; location ; california , united states } }', 'tointer': 'select the rows whose location record fuzzily matches to california , united states . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'california , united states'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record fuzzily matches to california , united states .', 'tostr': 'filter_eq { all_rows ; location ; california , united states }'}, 'opponent'], 'result': 'muhsin corbbrey', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; location ; california , united states } ; opponent }'}, 'muhsin corbbrey'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; location ; california , united states } ; opponent } ; muhsin corbbrey }', 'tointer': 'the opponent record of this unqiue row is muhsin corbbrey .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; location ; california , united states } } ; eq { hop { filter_eq { all_rows ; location ; california , united states } ; opponent } ; muhsin corbbrey } } = true', 'tointer': 'select the rows whose location record fuzzily matches to california , united states . there is only one such row in the table . the opponent record of this unqiue row is muhsin corbbrey .'} | and { only { filter_eq { all_rows ; location ; california , united states } } ; eq { hop { filter_eq { all_rows ; location ; california , united states } ; opponent } ; muhsin corbbrey } } = true | select the rows whose location record fuzzily matches to california , united states . there is only one such row in the table . the opponent record of this unqiue row is muhsin corbbrey . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'location_7': 7, 'california , united states_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'opponent_9': 9, 'muhsin corbbrey_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'location_7': 'location', 'california , united states_8': 'california , united states', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'opponent_9': 'opponent', 'muhsin corbbrey_10': 'muhsin corbbrey'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'location_7': [0], 'california , united states_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'opponent_9': [2], 'muhsin corbbrey_10': [3]} | ['res', 'record', 'opponent', 'method', 'event', 'round', 'location'] | [['win', '12 - 3', 'joe wilk', 'tko ( strikes )', 'strikeforce challengers : woodley vs bears', '1', 'kansas , united states'], ['loss', '11 - 3', 'muhsin corbbrey', 'decision ( unanimous )', 'shoxcjuly_27 .2 c_2007_card', '3', 'california , united states'], ['win', '11 - 2', 'talon hoffman', 'tko', 'ifo - eastman vs kimmons', '2', 'nevada , united states'], ['win', '10 - 2', 'kyle olsen', 'decision ( unanimous )', 'tuff - n - uff 2', '3', 'nevada , united states'], ['win', '9 - 2', 'tj brown', 'decision ( unanimous )', 'tuff - n - uff 2', '3', 'nevada , united states'], ['win', '8 - 2', 'frank young', 'submission', 'tfc 7 - red rumble', '1', 'kansas , united states'], ['loss', '7 - 2', 'luke gwaltney', 'decision ( unanimous )', 'ggp - good guys promotions', '3', 'kansas , united states'], ['win', '7 - 1', 'billy walters', 'tko', 'tfc 6 - titan fighting championships 6', '1', 'kansas , united states'], ['win', '6 - 1', 'mike funk', 'submission ( strikes )', 'fcf - freestyle cage fighting', '1', 'oklahoma , united states'], ['loss', '5 - 1', 'justin james', 'submission ( armbar )', 'ec 70 - extreme challenge 70', '1', 'wisconsin , united states'], ['win', '5 - 0', 'robert hembree', 'submission ( strikes )', 'tfc 5 - titan fighting championship 5', '1', 'kansas , united states'], ['win', '4 - 0', 'joe davis', 'tko', 'tfc 4 - memorial mayhem', '1', 'kansas , united states'], ['win', '3 - 0', 'nathan murdock', 'decision ( unanimous )', 'tfc 3 - red river rumble', '3', 'oklahoma , united states'], ['win', '2 - 0', 'adrian olivas', 'ko', 'ndn - promotions', '2', 'oklahoma , united states'], ['win', '1 - 0', 'bobby gregg', 'tko', 'iscf - clash of the titans', '1', 'missouri , united states']] |
soccer - specific stadium | https://en.wikipedia.org/wiki/Soccer-specific_stadium | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1034291-6.html.csv | majority | the majority of soccer-specific stadiums are for clubs that play in the pdl division . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'pdl', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'division', 'pdl'], 'result': True, 'ind': 0, 'tointer': 'for the division records of all rows , most of them fuzzily match to pdl .', 'tostr': 'most_eq { all_rows ; division ; pdl } = true'} | most_eq { all_rows ; division ; pdl } = true | for the division records of all rows , most of them fuzzily match to pdl . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'division_3': 3, 'pdl_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'division_3': 'division', 'pdl_4': 'pdl'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'division_3': [0], 'pdl_4': [0]} | ['stadium', 'club ( s )', 'division', 'city', 'capacity', 'opened'] | [['blackbaud stadium', 'charleston battery', 'usl pro', 'charleston , sc', '5113', '1999'], ['city park stadium', 'westchester flames', 'pdl', 'new rochelle , ny', '1845', '1970s'], ['seminole soccer complex ( sanford )', 'central florida kraze', 'pdl', 'lake mary , fl', '3666', '1995'], ['ezell park', 'nashville metros', 'pdl', 'nashville , tn', '1317', '1950s'], ['highmark stadium', 'pittsburgh riverhounds', 'usl pro', 'pittsburgh , pa', '3500', '2013'], ['indiana invaders soccer complex', 'indiana invaders', 'pdl', 'south bend , in', '4985', '2004'], ['legion stadium', 'wilmington hammerheads', 'usl pro', 'wilmington , nc', '5300', '1930s'], ['lusitano stadium', 'western mass pioneers', 'pdl', 'ludlow , ma', '3000', '1918'], ['macpherson stadium', 'carolina dynamo', 'pdl', 'browns summit , nc', '1600', '2002'], ['patriot stadium', 'chivas el paso patriots', 'pdl', 'el paso , tx', '3000', '2005'], ["sahlen 's stadium", 'rochester rhinos western new york flash', 'usl pro nwsl', 'rochester , ny', '13500', '2006'], ['virginia beach sportsplex', 'hampton roads piranhas', 'pdl', 'virginia beach , va', '10000', '1999']] |
turkmenistan fed cup team | https://en.wikipedia.org/wiki/Turkmenistan_Fed_Cup_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11311764-4.html.csv | comparative | amangul mollayeva recorded more ties than ayna ereshova on the turkmenistan fed cup team . | {'row_1': '6', 'row_2': '4', 'col': '3', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'amangul mollayeva'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to amangul mollayeva .', 'tostr': 'filter_eq { all_rows ; name ; amangul mollayeva }'}, 'ties'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; amangul mollayeva } ; ties }', 'tointer': 'select the rows whose name record fuzzily matches to amangul mollayeva . take the ties record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'ayna ereshova'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to ayna ereshova .', 'tostr': 'filter_eq { all_rows ; name ; ayna ereshova }'}, 'ties'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; ayna ereshova } ; ties }', 'tointer': 'select the rows whose name record fuzzily matches to ayna ereshova . take the ties record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; name ; amangul mollayeva } ; ties } ; hop { filter_eq { all_rows ; name ; ayna ereshova } ; ties } } = true', 'tointer': 'select the rows whose name record fuzzily matches to amangul mollayeva . take the ties record of this row . select the rows whose name record fuzzily matches to ayna ereshova . take the ties record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; name ; amangul mollayeva } ; ties } ; hop { filter_eq { all_rows ; name ; ayna ereshova } ; ties } } = true | select the rows whose name record fuzzily matches to amangul mollayeva . take the ties record of this row . select the rows whose name record fuzzily matches to ayna ereshova . take the ties record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'name_7': 7, 'amangul mollayeva_8': 8, 'ties_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'name_11': 11, 'ayna ereshova_12': 12, 'ties_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'name_7': 'name', 'amangul mollayeva_8': 'amangul mollayeva', 'ties_9': 'ties', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'name_11': 'name', 'ayna ereshova_12': 'ayna ereshova', 'ties_13': 'ties'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'name_7': [0], 'amangul mollayeva_8': [0], 'ties_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'name_11': [1], 'ayna ereshova_12': [1], 'ties_13': [3]} | ['name', 'tkm career', 'ties', 'dou w / l', 'sin w / l'] | [['anastasiya prenko', '2008 -', '18', '9 - 6', '10 - 7'], ['jenneta halliyeva', '2004 - 2013', '18', '5 - 6', '4 - 5'], ['ummarahmat hummetova', '2004 - 2012', '13', '3 - 8', '1 - 7'], ['ayna ereshova', '2011', '1', '1 - 0', '0 - 0'], ['guljahan kadryova', '2013', '2', '1 - 0', '0 - 1'], ['amangul mollayeva', '2011', '4', '1 - 0', '0 - 3'], ['jahana bayramova', '2013 -', '5', '1 - 1', '1 - 4'], ['veronika babayan', '2004', '3', '1 - 2', '0 - 1']] |
2003 - 04 european challenge cup | https://en.wikipedia.org/wiki/2003%E2%80%9304_European_Challenge_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27987767-3.html.csv | count | there are 3 players with a match point result of 4-0 . | {'scope': 'all', 'criterion': 'equal', 'value': '4-0', 'result': '3', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'match points', '4-0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose match points record fuzzily matches to 4-0 .', 'tostr': 'filter_eq { all_rows ; match points ; 4-0 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; match points ; 4-0 } }', 'tointer': 'select the rows whose match points record fuzzily matches to 4-0 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; match points ; 4-0 } } ; 3 } = true', 'tointer': 'select the rows whose match points record fuzzily matches to 4-0 . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; match points ; 4-0 } } ; 3 } = true | select the rows whose match points record fuzzily matches to 4-0 . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'match points_5': 5, '4-0_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'match points_5': 'match points', '4-0_6': '4-0', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'match points_5': [0], '4-0_6': [0], '3_7': [2]} | ['proceed to quarter - final', 'match points', 'aggregate score', 'points margin', 'eliminated from competition'] | [['nec harlequins', '4 - 0', '89 - 25', '64', 'montauban'], ['béziers', '4 - 0', '43 - 23', '20', 'grenoble'], ['bath', '4 - 0', '58 - 42', '16', 'colomiers'], ['connacht', '2 - 2', '35 - 17', '18', 'pau'], ['narbonne', '2 - 2', '42 - 30', '12', 'london irish'], ['brive', '2 - 2', '58 - 48', '10', 'castres olympique'], ['montferrand', '2 - 2', '28 - 23', '5', 'newcastle falcons']] |
fiba eurobasket 2009 squads | https://en.wikipedia.org/wiki/FIBA_EuroBasket_2009_squads | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23670057-5.html.csv | comparative | player fedor dmitriev was born earlier than player anton ponkrashov . | {'row_1': '7', 'row_2': '10', 'col': '6', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'fedor dmitriev'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to fedor dmitriev .', 'tostr': 'filter_eq { all_rows ; player ; fedor dmitriev }'}, 'year born'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; fedor dmitriev } ; year born }', 'tointer': 'select the rows whose player record fuzzily matches to fedor dmitriev . take the year born record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'anton ponkrashov'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to anton ponkrashov .', 'tostr': 'filter_eq { all_rows ; player ; anton ponkrashov }'}, 'year born'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; anton ponkrashov } ; year born }', 'tointer': 'select the rows whose player record fuzzily matches to anton ponkrashov . take the year born record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; player ; fedor dmitriev } ; year born } ; hop { filter_eq { all_rows ; player ; anton ponkrashov } ; year born } } = true', 'tointer': 'select the rows whose player record fuzzily matches to fedor dmitriev . take the year born record of this row . select the rows whose player record fuzzily matches to anton ponkrashov . take the year born record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; player ; fedor dmitriev } ; year born } ; hop { filter_eq { all_rows ; player ; anton ponkrashov } ; year born } } = true | select the rows whose player record fuzzily matches to fedor dmitriev . take the year born record of this row . select the rows whose player record fuzzily matches to anton ponkrashov . take the year born record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'player_7': 7, 'fedor dmitriev_8': 8, 'year born_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'anton ponkrashov_12': 12, 'year born_13': 13} | {'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'player_7': 'player', 'fedor dmitriev_8': 'fedor dmitriev', 'year born_9': 'year born', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'anton ponkrashov_12': 'anton ponkrashov', 'year born_13': 'year born'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'fedor dmitriev_8': [0], 'year born_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'anton ponkrashov_12': [1], 'year born_13': [3]} | ['no', 'player', 'height ( m )', 'height ( f )', 'position', 'year born', 'current club'] | [['4', 'andrey vorontsevich', '2.07', "6 ' 09", 'forward', '1987', 'cska moscow'], ['5', 'nikita kurbanov', '2.03', "6 ' 08", 'forward', '1986', 'cska moscow'], ['6', 'sergey bykov', '1.90', "6 ' 03", 'guard', '1983', 'lokomotiv kuban'], ['7', 'vitaly fridzon', '1.95', "6 ' 05", 'guard', '1985', 'khimki'], ['8', 'kelly mccarty', '2.01', "6 ' 07", 'forward', '1975', 'unics kazan'], ['9', 'dmitri sokolov', '2.14', "7 ' 00", 'center', '1985', 'cska moscow'], ['10', 'fedor dmitriev', '2.05', "6 ' 09", 'forward', '1984', 'spartak saint petersburg'], ['11', 'egor vyaltsev', '1.94', "6 ' 04", 'guard', '1985', 'khimki'], ['12', 'sergey monya', '2.05', "6 ' 09", 'forward', '1983', 'khimki'], ['13', 'anton ponkrashov', '2.00', "6 ' 07", 'guard', '1986', 'cska moscow'], ['14', 'alexey zozulin', '2.01', "6 ' 07", 'guard', '1983', 'cska moscow']] |
three rivers conference ( indiana ) | https://en.wikipedia.org/wiki/Three_Rivers_Conference_%28Indiana%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15176211-2.html.csv | count | two of the schools in the three rivers conference are located in marshall county . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'marshall', 'result': '2', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'county', 'marshall'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose county record fuzzily matches to marshall .', 'tostr': 'filter_eq { all_rows ; county ; marshall }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; county ; marshall } }', 'tointer': 'select the rows whose county record fuzzily matches to marshall . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; county ; marshall } } ; 2 } = true', 'tointer': 'select the rows whose county record fuzzily matches to marshall . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; county ; marshall } } ; 2 } = true | select the rows whose county record fuzzily matches to marshall . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'county_5': 5, 'marshall_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'county_5': 'county', 'marshall_6': 'marshall', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'county_5': [0], 'marshall_6': [0], '2_7': [2]} | ['school', 'location', 'mascot', 'county', 'year joined', 'previous conference', 'year left', 'new conference'] | [['caston', 'fulton', 'comets', '25 fulton', '1971', 'independents', '1978', 'joined midwest'], ['culver community', 'culver', 'cavaliers', '50 marshall', '1971', 'independents', '1976', 'independents'], ['triton', 'bourbon', 'trojans', '50 marshall', '1971', 'independent', '1980', 'joined northern state'], ['eastern ( greentown )', 'greentown', 'comets', '34 howard', '1980', 'mid - indiana', '1987', 'joined mid - indiana'], ['oak hill', 'converse', 'golden eagles', '27 grant', '1980', 'mid - indiana', '2006', 'joined central indiana']] |
1978 new orleans saints season | https://en.wikipedia.org/wiki/1978_New_Orleans_Saints_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18842963-2.html.csv | aggregation | during the 1978 new orleans saints ’ season , the average attendance during the month of november was 57,973 . | {'scope': 'subset', 'col': '5', 'type': 'average', 'result': '57973', 'subset': {'col': '2', 'criterion': 'fuzzily_match', 'value': 'november'}} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'november'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; november }', 'tointer': 'select the rows whose date record fuzzily matches to november .'}, 'attendance'], 'result': '57973', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; date ; november } ; attendance }'}, '57973'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; date ; november } ; attendance } ; 57973 } = true', 'tointer': 'select the rows whose date record fuzzily matches to november . the average of the attendance record of these rows is 57973 .'} | round_eq { avg { filter_eq { all_rows ; date ; november } ; attendance } ; 57973 } = true | select the rows whose date record fuzzily matches to november . the average of the attendance record of these rows is 57973 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'date_5': 5, 'november_6': 6, 'attendance_7': 7, '57973_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'date_5': 'date', 'november_6': 'november', 'attendance_7': 'attendance', '57973_8': '57973'} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'date_5': [0], 'november_6': [0], 'attendance_7': [1], '57973_8': [2]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 3 , 1978', 'minnesota vikings', 'w 31 - 24', '54187'], ['2', 'september 10 , 1978', 'green bay packers', 'l 28 - 17', '54336'], ['3', 'september 17 , 1978', 'philadelphia eagles', 'l 24 - 17', '49242'], ['4', 'september 24 , 1978', 'cincinnati bengals', 'w 20 - 18', '40455'], ['5', 'october 1 , 1978', 'los angeles rams', 'l 26 - 20', '61659'], ['6', 'october 8 , 1978', 'cleveland browns', 'l 24 - 16', '50158'], ['7', 'october 15 , 1978', 'san francisco 49ers', 'w 14 - 7', '37671'], ['8', 'october 22 , 1978', 'los angeles rams', 'w 10 - 3', '47574'], ['9', 'october 29 , 1978', 'new york giants', 'w 28 - 17', '59807'], ['10', 'november 5 , 1978', 'pittsburgh steelers', 'l 20 - 14', '48526'], ['11', 'november 12 , 1978', 'atlanta falcons', 'l 20 - 17', '70323'], ['12', 'november 19 , 1978', 'dallas cowboys', 'l 27 - 7', '57920'], ['13', 'november 26 , 1978', 'atlanta falcons', 'l 20 - 17', '55121'], ['14', 'december 3 , 1978', 'san francisco 49ers', 'w 24 - 13', '50068'], ['15', 'december 10 , 1978', 'houston oilers', 'l 17 - 12', '63169'], ['16', 'december 17 , 1978', 'tampa bay buccaneers', 'w 17 - 10', '51207']] |
1945 vfl season | https://en.wikipedia.org/wiki/1945_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10809271-16.html.csv | majority | in the 1945 vfl season , all the games featuring south melbourne achieved a crowd greater than 20000 . | {'scope': 'subset', 'col': '6', 'most_or_all': 'all', 'criterion': 'greater_than', 'value': '20000', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'south melbourne'}} | {'func': 'all_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'away team', 'south melbourne'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; away team ; south melbourne }', 'tointer': 'select the rows whose away team record fuzzily matches to south melbourne .'}, 'crowd', '20000'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose away team record fuzzily matches to south melbourne . for the crowd records of these rows , all of them are greater than 20000 .', 'tostr': 'all_greater { filter_eq { all_rows ; away team ; south melbourne } ; crowd ; 20000 } = true'} | all_greater { filter_eq { all_rows ; away team ; south melbourne } ; crowd ; 20000 } = true | select the rows whose away team record fuzzily matches to south melbourne . for the crowd records of these rows , all of them are greater than 20000 . | 2 | 2 | {'all_greater_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'away team_4': 4, 'south melbourne_5': 5, 'crowd_6': 6, '20000_7': 7} | {'all_greater_1': 'all_greater', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'away team_4': 'away team', 'south melbourne_5': 'south melbourne', 'crowd_6': 'crowd', '20000_7': '20000'} | {'all_greater_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'away team_4': [0], 'south melbourne_5': [0], 'crowd_6': [1], '20000_7': [1]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['geelong', '7.14 ( 56 )', 'melbourne', '17.13 ( 115 )', 'kardinia park', '7000', '4 august 1945'], ['footscray', '7.13 ( 55 )', 'south melbourne', '8.8 ( 56 )', 'western oval', '27000', '4 august 1945'], ['collingwood', '16.8 ( 104 )', 'essendon', '10.15 ( 75 )', 'victoria park', '19000', '4 august 1945'], ['richmond', '15.15 ( 105 )', 'hawthorn', '19.7 ( 121 )', 'punt road oval', '13000', '4 august 1945'], ['north melbourne', '12.6 ( 78 )', 'fitzroy', '4.9 ( 33 )', 'arden street oval', '14000', '4 august 1945'], ['st kilda', '7.15 ( 57 )', 'carlton', '11.13 ( 79 )', 'junction oval', '10000', '4 august 1945']] |
malayalam calendar | https://en.wikipedia.org/wiki/Malayalam_calendar | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-169955-1.html.csv | comparative | the month of chingam occurs before the month of tulam . | {'row_1': '1', 'row_2': '3', 'col': '3', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'months in malayalam era', 'chingam'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose months in malayalam era record fuzzily matches to chingam .', 'tostr': 'filter_eq { all_rows ; months in malayalam era ; chingam }'}, 'gregorian calendar'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; months in malayalam era ; chingam } ; gregorian calendar }', 'tointer': 'select the rows whose months in malayalam era record fuzzily matches to chingam . take the gregorian calendar record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'months in malayalam era', 'tulam'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose months in malayalam era record fuzzily matches to tulam .', 'tostr': 'filter_eq { all_rows ; months in malayalam era ; tulam }'}, 'gregorian calendar'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; months in malayalam era ; tulam } ; gregorian calendar }', 'tointer': 'select the rows whose months in malayalam era record fuzzily matches to tulam . take the gregorian calendar record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; months in malayalam era ; chingam } ; gregorian calendar } ; hop { filter_eq { all_rows ; months in malayalam era ; tulam } ; gregorian calendar } } = true', 'tointer': 'select the rows whose months in malayalam era record fuzzily matches to chingam . take the gregorian calendar record of this row . select the rows whose months in malayalam era record fuzzily matches to tulam . take the gregorian calendar record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; months in malayalam era ; chingam } ; gregorian calendar } ; hop { filter_eq { all_rows ; months in malayalam era ; tulam } ; gregorian calendar } } = true | select the rows whose months in malayalam era record fuzzily matches to chingam . take the gregorian calendar record of this row . select the rows whose months in malayalam era record fuzzily matches to tulam . take the gregorian calendar record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'months in malayalam era_7': 7, 'chingam_8': 8, 'gregorian calendar_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'months in malayalam era_11': 11, 'tulam_12': 12, 'gregorian calendar_13': 13} | {'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'months in malayalam era_7': 'months in malayalam era', 'chingam_8': 'chingam', 'gregorian calendar_9': 'gregorian calendar', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'months in malayalam era_11': 'months in malayalam era', 'tulam_12': 'tulam', 'gregorian calendar_13': 'gregorian calendar'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'months in malayalam era_7': [0], 'chingam_8': [0], 'gregorian calendar_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'months in malayalam era_11': [1], 'tulam_12': [1], 'gregorian calendar_13': [3]} | ['months in malayalam era', 'in malayalam', 'gregorian calendar', 'tamil calendar', 'saka era', 'sign of zodiac'] | [['chingam', 'ചിങ ങ', 'august - september', 'aavani', 'sravan - bhadrapada', 'leo'], ['kanni', 'കന നി', 'september - october', 'purattasi', 'bhadrapada - asvina', 'virgo'], ['tulam', 'തുലാ', 'october - november', 'aippasi', 'asvina - kartika', 'libra'], ['vrscikam', 'വൃശ ചിക', 'november - december', 'karthigai', 'kartika - agrahayana', 'scorpio'], ['dhanu', 'ധനു', 'december - january', 'margazhi', 'agrahayana - pausa', 'sagittarius'], ['makaram', 'മകര', 'january - february', 'thai', 'pausa - magha', 'capricon'], ['kumbham', 'കു ഭ', 'february - march', 'maasi', 'magha - phalguna', 'aquarius'], ['minam', 'മീന', 'march - april', 'panguni', 'phalguna - chaitra', 'pisces'], ['medam', 'മേട', 'april - may', 'chithirai', 'chaitra - vaisakha', 'aries'], ['edavam ( idavam )', 'ഇടവ', 'may - june', 'vaikasi', 'vaisakha - jyaistha', 'taurus'], ['mithunam', 'മിഥുന', 'june - july', 'aani', 'jyaistha - asada', 'gemini'], ['karkadakam', 'കര ക കടക', 'july - august', 'aadi', 'asada - sravana', 'cancer']] |
1991 pga championship | https://en.wikipedia.org/wiki/1991_PGA_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18130923-1.html.csv | unique | in the 1991 pga championship , of the players from the united states , the only one that was 1 under par was jack nicklaus . | {'scope': 'subset', 'row': '4', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': '-1', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'united states'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'united states'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; country ; united states }', 'tointer': 'select the rows whose country record fuzzily matches to united states .'}, 'to par', '-1'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose to par record is equal to -1 .', 'tostr': 'filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; -1 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; -1 } }', 'tointer': 'select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose to par record is equal to -1 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'united states'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; country ; united states }', 'tointer': 'select the rows whose country record fuzzily matches to united states .'}, 'to par', '-1'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose to par record is equal to -1 .', 'tostr': 'filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; -1 }'}, 'player'], 'result': 'jack nicklaus', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; -1 } ; player }'}, 'jack nicklaus'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; -1 } ; player } ; jack nicklaus }', 'tointer': 'the player record of this unqiue row is jack nicklaus .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; -1 } } ; eq { hop { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; -1 } ; player } ; jack nicklaus } } = true', 'tointer': 'select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose to par record is equal to -1 . there is only one such row in the table . the player record of this unqiue row is jack nicklaus .'} | and { only { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; -1 } } ; eq { hop { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; -1 } ; player } ; jack nicklaus } } = true | select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose to par record is equal to -1 . there is only one such row in the table . the player record of this unqiue row is jack nicklaus . | 8 | 6 | {'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'country_8': 8, 'united states_9': 9, 'to par_10': 10, '-1_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'player_12': 12, 'jack nicklaus_13': 13} | {'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_eq_1': 'filter_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'country_8': 'country', 'united states_9': 'united states', 'to par_10': 'to par', '-1_11': '-1', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'player_12': 'player', 'jack nicklaus_13': 'jack nicklaus'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'country_8': [0], 'united states_9': [0], 'to par_10': [1], '-1_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'player_12': [3], 'jack nicklaus_13': [4]} | ['player', 'country', 'year ( s ) won', 'total', 'to par', 'finish'] | [['raymond floyd', 'united states', '1969 , 1982', '284', '- 4', 't7'], ['hal sutton', 'united states', '1983', '284', '- 4', 't7'], ['payne stewart', 'united states', '1989', '285', '- 3', 't13'], ['jack nicklaus', 'united states', '1963 , 1971 , 1973 1975 , 1980', '287', '- 1', 't23'], ['wayne grady', 'australia', '1990', '290', '+ 2', 't43'], ['lanny wadkins', 'united states', '1977', '290', '+ 2', 't43'], ['david graham', 'australia', '1979', '292', '+ 4', 't52'], ['jeff sluman', 'united states', '1988', '294', '+ 6', 't61'], ['bob tway', 'united states', '1986', '296', '+ 8', 't66']] |
sparc enterprise | https://en.wikipedia.org/wiki/SPARC_Enterprise | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-10818465-1.html.csv | ordinal | in sparc enterprise t1000 model has the least max memory in those whose max processors is 1 ultrasparc t1 . | {'scope': 'subset', 'row': '2', 'col': '5', 'order': '1', 'col_other': '1', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'subset': {'col': '3', 'criterion': 'equal', 'value': '1 ultrasparc t1'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'max processors', '1 ultrasparc t1'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; max processors ; 1 ultrasparc t1 }', 'tointer': 'select the rows whose max processors record fuzzily matches to 1 ultrasparc t1 .'}, 'max memory', '1'], 'result': None, 'ind': 1, 'tostr': 'nth_argmin { filter_eq { all_rows ; max processors ; 1 ultrasparc t1 } ; max memory ; 1 }'}, 'model'], 'result': 't1000', 'ind': 2, 'tostr': 'hop { nth_argmin { filter_eq { all_rows ; max processors ; 1 ultrasparc t1 } ; max memory ; 1 } ; model }'}, 't1000'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmin { filter_eq { all_rows ; max processors ; 1 ultrasparc t1 } ; max memory ; 1 } ; model } ; t1000 } = true', 'tointer': 'select the rows whose max processors record fuzzily matches to 1 ultrasparc t1 . select the row whose max memory record of these rows is 1st minimum . the model record of this row is t1000 .'} | eq { hop { nth_argmin { filter_eq { all_rows ; max processors ; 1 ultrasparc t1 } ; max memory ; 1 } ; model } ; t1000 } = true | select the rows whose max processors record fuzzily matches to 1 ultrasparc t1 . select the row whose max memory record of these rows is 1st minimum . the model record of this row is t1000 . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmin_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'max processors_6': 6, '1 ultrasparc t1_7': 7, 'max memory_8': 8, '1_9': 9, 'model_10': 10, 't1000_11': 11} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmin_1': 'nth_argmin', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'max processors_6': 'max processors', '1 ultrasparc t1_7': '1 ultrasparc t1', 'max memory_8': 'max memory', '1_9': '1', 'model_10': 'model', 't1000_11': 't1000'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmin_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'max processors_6': [0], '1 ultrasparc t1_7': [0], 'max memory_8': [1], '1_9': [1], 'model_10': [2], 't1000_11': [3]} | ['model', 'ru', 'max processors', 'processor frequency', 'max memory', 'max disk capacity', 'ga date'] | [['m3000', '2', '1 sparc64 vii or vii +', '2.52 , 2.75 ghz ( vii ) or 2.86 ghz ( vii + )', '64 gb', '4 2.5 sas', 'october 2008 ( vii ) , april 2011 ( vii + )'], ['t1000', '1', '1 ultrasparc t1', '1.0 ghz', '32 gb', 'one 3.5 sata or two 2.5 sas', 'march 2006'], ['t2000', '2', '1 ultrasparc t1', '1.0 , 1.2 , 1.4 ghz', '64 gb', 'up to four 2.5 sas', 'december 2005'], ['t5120', '1', '1 ultrasparc t2', '1.2 , 1.4 ghz', '128 gb', 'up to eight 2.5 sas', 'november 2007'], ['t5140', '1', '2 ultrasparc t2 +', '1.2 , 1.4 ghz', '128 gb', 'up to eight 2.5 sas', 'april 2008'], ['t5220', '2', '1 ultrasparc t2', '1.2 , 1.4 ghz', '128 gb', 'up to sixteen 2.5 sas', 'november 2007'], ['t5240', '2', '2 ultrasparc t2 +', '1.2 , 1.4 ghz', '256 gb', 'up to sixteen 2.5 sas', 'april 2008']] |
1984 masters tournament | https://en.wikipedia.org/wiki/1984_Masters_Tournament | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16488662-1.html.csv | unique | ben crenshaw was the only player who earned over 100000 dollars in prize money in the 1984 masters . | {'scope': 'all', 'row': '1', 'col': '6', 'col_other': '2', 'criterion': 'greater_than', 'value': '100000', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'money', '100000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose money record is greater than 100000 .', 'tostr': 'filter_greater { all_rows ; money ; 100000 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; money ; 100000 } }', 'tointer': 'select the rows whose money record is greater than 100000 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'money', '100000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose money record is greater than 100000 .', 'tostr': 'filter_greater { all_rows ; money ; 100000 }'}, 'player'], 'result': 'ben crenshaw', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; money ; 100000 } ; player }'}, 'ben crenshaw'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; money ; 100000 } ; player } ; ben crenshaw }', 'tointer': 'the player record of this unqiue row is ben crenshaw .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; money ; 100000 } } ; eq { hop { filter_greater { all_rows ; money ; 100000 } ; player } ; ben crenshaw } } = true', 'tointer': 'select the rows whose money record is greater than 100000 . there is only one such row in the table . the player record of this unqiue row is ben crenshaw .'} | and { only { filter_greater { all_rows ; money ; 100000 } } ; eq { hop { filter_greater { all_rows ; money ; 100000 } ; player } ; ben crenshaw } } = true | select the rows whose money record is greater than 100000 . there is only one such row in the table . the player record of this unqiue row is ben crenshaw . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'money_7': 7, '100000_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'ben crenshaw_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'money_7': 'money', '100000_8': '100000', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'ben crenshaw_10': 'ben crenshaw'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'money_7': [0], '100000_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'ben crenshaw_10': [3]} | ['place', 'player', 'country', 'score', 'to par', 'money'] | [['1', 'ben crenshaw', 'united states', '67 + 72 + 70 + 68 = 277', '- 11', '108000'], ['2', 'tom watson', 'united states', '74 + 67 + 69 + 69 = 279', '- 9', '64800'], ['t3', 'david edwards', 'united states', '71 + 70 + 72 + 67 = 280', '- 8', '34800'], ['t3', 'gil morgan', 'united states', '73 + 71 + 69 + 67 = 280', '- 8', '34800'], ['5', 'larry nelson', 'united states', '76 + 69 + 66 + 70 = 281', '- 7', '24000'], ['t6', 'ronnie black', 'united states', '71 + 74 + 69 + 68 = 282', '- 6', '19425'], ['t6', 'david graham', 'australia', '69 + 70 + 70 + 73 = 282', '- 6', '19425'], ['t6', 'tom kite', 'united states', '70 + 68 + 69 + 75 = 282', '- 6', '19425'], ['t6', 'mark lye', 'united states', '69 + 66 + 73 + 74 = 282', '- 6', '19425'], ['10', 'fred couples', 'united states', '71 + 73 + 67 + 72 = 283', '- 5', '16200']] |
dancing with the stars ( u.s. season 3 ) | https://en.wikipedia.org/wiki/Dancing_with_the_Stars_%28U.S._season_3%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10535525-3.html.csv | majority | the majority of dancers who won scored at least 29 points on their dances . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'greater_than_eq', 'value': '29', 'subset': None} | {'func': 'most_greater_eq', 'args': ['all_rows', 'best score', '29'], 'result': True, 'ind': 0, 'tointer': 'for the best score records of all rows , most of them are greater than or equal to 29 .', 'tostr': 'most_greater_eq { all_rows ; best score ; 29 } = true'} | most_greater_eq { all_rows ; best score ; 29 } = true | for the best score records of all rows , most of them are greater than or equal to 29 . | 1 | 1 | {'most_greater_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'best score_3': 3, '29_4': 4} | {'most_greater_eq_0': 'most_greater_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'best score_3': 'best score', '29_4': '29'} | {'most_greater_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'best score_3': [0], '29_4': [0]} | ['dance', 'best dancer', 'best score', 'worst dancer', 'worst score'] | [['cha - cha - cha', 'emmitt smith', '30', 'tucker carlson', '12'], ['foxtrot', 'mario lopez joey lawrence', '29', 'sara evans', '15'], ['mambo', 'emmitt smith', '30', 'sara evans', '21'], ['quickstep', 'joey lawrence', '29', 'jerry springer', '19'], ['jive', 'monique coleman mario lopez', '27', 'joey lawrence willa ford', '22'], ['tango', 'mario lopez', '30', 'emmitt smith', '19'], ['waltz', 'emmitt smith', '29', 'jerry springer', '22'], ['rumba', 'joey lawrence', '30', 'joey lawrence', '24'], ['paso doble', 'mario lopez', '30', 'jerry springer', '18'], ['samba', 'emmitt smith', '30', 'monique coleman', '23'], ['freestyle', 'mario lopez', '30', 'emmitt smith', '29']] |
vice president of south korea | https://en.wikipedia.org/wiki/Vice_President_of_South_Korea | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1712482-2.html.csv | majority | most of the political parties where from the democratic party . | {'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'democratic party', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'political party', 'democratic party'], 'result': True, 'ind': 0, 'tointer': 'for the political party records of all rows , most of them fuzzily match to democratic party .', 'tostr': 'most_eq { all_rows ; political party ; democratic party } = true'} | most_eq { all_rows ; political party ; democratic party } = true | for the political party records of all rows , most of them fuzzily match to democratic party . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'political party_3': 3, 'democratic party_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'political party_3': 'political party', 'democratic party_4': 'democratic party'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'political party_3': [0], 'democratic party_4': [0]} | ['president', 'vice', 'romanized ( hangul )', 'took office', 'left office', 'political party'] | [['syngman rhee', '1', 'yi si - yeong ( 이시영 )', '24 july 1948', '9 may 1951 ( resign )', 'korea democratic party'], ['syngman rhee', '2', 'kim seong - su ( 김성수 )', '17 may 1951', '29 may 1952 ( resign )', 'korea democratic party'], ['syngman rhee', '3', 'hahm tae - young ( 함태영 )', '15 june 1952', '14 august 1956', 'independent'], ['syngman rhee', '4', 'chang myon ( 장면 )', '15 august 1956', '23 april 1960 ( resign )', 'democratic party'], ['syngman rhee', '5', 'yun bo - seon ( 윤보선 )', '23 april 1960', '26 april 1960', 'democratic party'], ['yun bo - seon', '6', 'heo jeong ( 윤보선 )', '13 august 1960', '16 august 1960', 'democratic party'], ['yun bo - seon', '7', 'song yo - chan ( 송요찬 )', '16 august 1960', '19 august 1960', 'military']] |
2002 - 03 toronto raptors season | https://en.wikipedia.org/wiki/2002%E2%80%9303_Toronto_Raptors_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15780718-5.html.csv | count | four raptors players had high point of 27 during the 2002-03 season . | {'scope': 'all', 'criterion': 'equal', 'value': '27', 'result': '4', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'high points', '27'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose high points record is equal to 27 .', 'tostr': 'filter_eq { all_rows ; high points ; 27 }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; high points ; 27 } }', 'tointer': 'select the rows whose high points record is equal to 27 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; high points ; 27 } } ; 4 } = true', 'tointer': 'select the rows whose high points record is equal to 27 . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; high points ; 27 } } ; 4 } = true | select the rows whose high points record is equal to 27 . the number of such rows is 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'high points_5': 5, '27_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'high points_5': 'high points', '27_6': '27', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'high points_5': [0], '27_6': [0], '4_7': [2]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'location attendance', 'record'] | [['16', 'december 1', 'memphis', 'w 92 - 87 ( ot )', 'vince carter ( 27 )', 'antonio davis ( 12 )', 'pyramid arena 13213', '6 - 10'], ['17', 'december 2', 'dallas', 'l 102 - 113 ( ot )', 'alvin williams ( 27 )', 'antonio davis ( 9 )', 'american airlines center 19696', '6 - 11'], ['18', 'december 4', 'new orleans', 'l 74 - 89 ( ot )', 'vince carter ( 26 )', 'antonio davis ( 11 )', 'new orleans arena 13342', '6 - 12'], ['19', 'december 6', 'chicago', 'w 103 - 89 ( ot )', 'voshon lenard ( 23 )', 'vince carter ( 10 )', 'air canada centre 18862', '7 - 12'], ['20', 'december 8', 'portland', 'l 91 - 104 ( ot )', 'vince carter ( 25 )', 'michael bradley , jerome williams ( 10 )', 'air canada centre 18645', '7 - 13'], ['21', 'december 11', 'cleveland', 'l 83 - 96 ( ot )', 'voshon lenard ( 24 )', 'jerome williams ( 8 )', 'gund arena 9090', '7 - 14'], ['22', 'december 13', 'seattle', 'l 79 - 91 ( ot )', 'lindsey hunter ( 21 )', 'jelani mccoy , morris peterson ( 7 )', 'air canada centre 18111', '7 - 15'], ['23', 'december 15', 'washington', 'l 82 - 95 ( ot )', 'lindsey hunter ( 22 )', 'jelani mccoy ( 10 )', 'air canada centre 19800', '7 - 16'], ['24', 'december 17', 'milwaukee', 'w 122 - 117 ( ot )', 'voshon lenard ( 23 )', 'nate huffman , jelani mccoy ( 9 )', 'bradley center 15926', '8 - 16'], ['25', 'december 18', 'chicago', 'l 83 - 96 ( ot )', 'morris peterson ( 22 )', 'jelani mccoy , jerome williams ( 11 )', 'united center 16111', '8 - 17'], ['26', 'december 20', 'miami', 'l 77 - 97 ( ot )', 'voshon lenard ( 19 )', 'jerome williams ( 9 )', 'air canada centre 19235', '8 - 18'], ['27', 'december 22', 'la lakers', 'l 107 - 109 ( ot )', 'morris peterson ( 27 )', 'jerome williams ( 12 )', 'air canada centre 19800', '8 - 19'], ['28', 'december 26', 'seattle', 'l 88 - 97 ( ot )', 'voshon lenard ( 27 )', 'jerome williams ( 11 )', 'keyarena 16139', '8 - 20'], ['29', 'december 27', 'golden state', 'l 96 - 101 ( ot )', 'voshon lenard , alvin williams ( 17 )', 'michael bradley ( 15 )', 'the arena in oakland 16487', '8 - 21'], ['30', 'december 29', 'la lakers', 'l 88 - 104 ( ot )', 'morris peterson ( 18 )', 'jelani mccoy , morris peterson ( 5 )', 'staples center 18997', '8 - 22'], ['31', 'december 30', 'utah', 'l 85 - 107 ( ot )', 'voshon lenard , morris peterson ( 18 )', 'jelani mccoy , morris peterson ( 6 )', 'delta center 19911', '8 - 23']] |
into the woods | https://en.wikipedia.org/wiki/Into_the_Woods | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15341-3.html.csv | count | into the woods was nominated for seven laurence olivier awards . | {'scope': 'all', 'criterion': 'equal', 'value': 'laurence olivier award', 'result': '7', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'award', 'laurence olivier award'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose award record fuzzily matches to laurence olivier award .', 'tostr': 'filter_eq { all_rows ; award ; laurence olivier award }'}], 'result': '7', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; award ; laurence olivier award } }', 'tointer': 'select the rows whose award record fuzzily matches to laurence olivier award . the number of such rows is 7 .'}, '7'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; award ; laurence olivier award } } ; 7 } = true', 'tointer': 'select the rows whose award record fuzzily matches to laurence olivier award . the number of such rows is 7 .'} | eq { count { filter_eq { all_rows ; award ; laurence olivier award } } ; 7 } = true | select the rows whose award record fuzzily matches to laurence olivier award . the number of such rows is 7 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'award_5': 5, 'laurence olivier award_6': 6, '7_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'award_5': 'award', 'laurence olivier award_6': 'laurence olivier award', '7_7': '7'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'award_5': [0], 'laurence olivier award_6': [0], '7_7': [2]} | ['year', 'award', 'category', 'nominee', 'result'] | [['1991', 'laurence olivier award', 'best new musical', 'best new musical', 'nominated'], ['1991', 'laurence olivier award', 'best director of a musical', 'richard jones', 'won'], ['1991', 'laurence olivier award', 'best actor in a musical', 'ian bartholomew', 'nominated'], ['1991', 'laurence olivier award', 'best actress in a musical', 'imelda staunton', 'won'], ['1991', 'laurence olivier award', 'best actress in a musical', 'julia mckenzie', 'nominated'], ['1991', 'laurence olivier award', 'best performance in a supporting role in a musical', 'clive carter', 'nominated'], ['1991', 'laurence olivier award', 'best costume design', 'sue blane', 'nominated']] |
1983 - 84 segunda división | https://en.wikipedia.org/wiki/1983%E2%80%9384_Segunda_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12138116-2.html.csv | count | three teams had 39 goals scored against them . | {'scope': 'all', 'criterion': 'equal', 'value': '39', 'result': '3', 'col': '9', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'goals against', '39'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose goals against record is equal to 39 .', 'tostr': 'filter_eq { all_rows ; goals against ; 39 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; goals against ; 39 } }', 'tointer': 'select the rows whose goals against record is equal to 39 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; goals against ; 39 } } ; 3 } = true', 'tointer': 'select the rows whose goals against record is equal to 39 . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; goals against ; 39 } } ; 3 } = true | select the rows whose goals against record is equal to 39 . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'goals against_5': 5, '39_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'goals against_5': 'goals against', '39_6': '39', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'goals against_5': [0], '39_6': [0], '3_7': [2]} | ['position', 'club', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference'] | [['1', 'castilla cf 1', '38', '50 + 12', '19', '12', '7', '69', '47', '+ 22'], ['2', 'bilbao athletic 2', '38', '50 + 12', '20', '10', '8', '61', '39', '+ 22'], ['3', 'hércules cf', '38', '45 + 7', '16', '13', '9', '46', '35', '+ 11'], ['4', 'racing de santander', '38', '44 + 6', '16', '12', '10', '53', '39', '+ 14'], ['5', 'elche cf', '38', '43 + 5', '16', '11', '11', '64', '40', '+ 24'], ['6', 'celta de vigo', '38', '42 + 4', '15', '12', '11', '45', '36', '+ 9'], ['7', 'barcelona atlètic', '38', '40 + 2', '14', '12', '12', '55', '48', '+ 7'], ['8', 'granada cf', '38', '40 + 2', '15', '10', '13', '42', '36', '+ 6'], ['9', 'deportivo de la coruña', '38', '39 + 1', '14', '11', '13', '37', '39', '- 2'], ['10', 'cd castellón', '38', '37 - 1', '14', '9', '15', '45', '57', '- 12'], ['11', 'ud las palmas', '38', '36 - 2', '12', '12', '14', '46', '52', '- 6'], ['12', 'recreativo de huelva', '38', '36 - 2', '12', '12', '14', '32', '48', '- 16'], ['13', 'real oviedo', '38', '35 - 3', '13', '9', '16', '44', '49', '- 5'], ['14', 'atlético madrileño', '38', '34 - 4', '12', '10', '16', '64', '63', '+ 1'], ['15', 'cd tenerife', '38', '34 - 4', '11', '12', '15', '45', '49', '- 4'], ['16', 'cartagena fc', '38', '33 - 5', '9', '15', '14', '35', '46', '- 11'], ['17', 'linares cf', '38', '32 - 6', '11', '10', '17', '39', '55', '- 24'], ['18', 'algeciras cf', '38', '32 - 6', '10', '12', '16', '34', '45', '- 11'], ['19', 'palencia cf', '38', '29 - 9', '8', '13', '17', '33', '49', '- 16'], ['20', 'rayo vallecano', '38', '29 - 9', '7', '15', '16', '34', '51', '- 17']] |
wru division five south east | https://en.wikipedia.org/wiki/WRU_Division_Five_South_East | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17625749-3.html.csv | count | among the clubs of the wru division five south east that won more than 10 games in the 2007-2008 season , 2 of them lost 3 games each . | {'scope': 'subset', 'criterion': 'equal', 'value': '3', 'result': '2', 'col': '5', 'subset': {'col': '3', 'criterion': 'greater_than', 'value': '10'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'won', '10'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; won ; 10 }', 'tointer': 'select the rows whose won record is greater than 10 .'}, 'lost', '3'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose won record is greater than 10 . among these rows , select the rows whose lost record is equal to 3 .', 'tostr': 'filter_eq { filter_greater { all_rows ; won ; 10 } ; lost ; 3 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_greater { all_rows ; won ; 10 } ; lost ; 3 } }', 'tointer': 'select the rows whose won record is greater than 10 . among these rows , select the rows whose lost record is equal to 3 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_greater { all_rows ; won ; 10 } ; lost ; 3 } } ; 2 } = true', 'tointer': 'select the rows whose won record is greater than 10 . among these rows , select the rows whose lost record is equal to 3 . the number of such rows is 2 .'} | eq { count { filter_eq { filter_greater { all_rows ; won ; 10 } ; lost ; 3 } } ; 2 } = true | select the rows whose won record is greater than 10 . among these rows , select the rows whose lost record is equal to 3 . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_eq_1': 1, 'filter_greater_0': 0, 'all_rows_5': 5, 'won_6': 6, '10_7': 7, 'lost_8': 8, '3_9': 9, '2_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_eq_1': 'filter_eq', 'filter_greater_0': 'filter_greater', 'all_rows_5': 'all_rows', 'won_6': 'won', '10_7': '10', 'lost_8': 'lost', '3_9': '3', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_eq_1': [2], 'filter_greater_0': [1], 'all_rows_5': [0], 'won_6': [0], '10_7': [0], 'lost_8': [1], '3_9': [1], '2_10': [3]} | ['club', 'played', 'won', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus', 'losing bonus', 'points'] | [['club', 'played', 'won', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus', 'losing bonus', 'points'], ['porth harlequins rfc', '20', '17', '0', '3', '642', '173', '100', '19', '12', '2', '82'], ["st joseph 's rfc", '20', '17', '0', '3', '503', '179', '69', '17', '9', '3', '80'], ['pontyclun rfc', '20', '14', '1', '5', '468', '218', '66', '24', '7', '2', '67'], ['deri rfc', '20', '14', '0', '6', '476', '285', '65', '33', '7', '3', '66'], ['st albans rfc', '20', '11', '0', '9', '402', '423', '58', '61', '7', '1', '52'], ['cowbridge rfc', '20', '8', '0', '12', '329', '379', '37', '54', '3', '7', '42'], ['old penarthians rfc', '20', '9', '0', '11', '231', '369', '29', '53', '2', '3', '41'], ['penygraig rfc', '20', '6', '1', '13', '260', '436', '30', '63', '2', '5', '33'], ['ogmore vale rfc', '20', '6', '0', '14', '208', '475', '27', '71', '2', '3', '29'], ['canton rfc', '20', '4', '0', '16', '248', '499', '34', '67', '3', '6', '25'], ['dinas powys rfc', '20', '3', '0', '17', '161', '492', '20', '73', '1', '1', '14']] |
who do you think you are ? ( canadian tv series ) | https://en.wikipedia.org/wiki/Who_Do_You_Think_You_Are%3F_%28Canadian_TV_series%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11642945-1.html.csv | ordinal | margot kidder was the subject of the who do you think you are ? episode of the second-earliest original airing date . | {'row': '2', 'col': '4', 'order': '2', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'original air date', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; original air date ; 2 }'}, 'celebrity'], 'result': 'margot kidder', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; original air date ; 2 } ; celebrity }'}, 'margot kidder'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; original air date ; 2 } ; celebrity } ; margot kidder } = true', 'tointer': 'select the row whose original air date record of all rows is 2nd minimum . the celebrity record of this row is margot kidder .'} | eq { hop { nth_argmin { all_rows ; original air date ; 2 } ; celebrity } ; margot kidder } = true | select the row whose original air date record of all rows is 2nd minimum . the celebrity record of this row is margot kidder . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'original air date_5': 5, '2_6': 6, 'celebrity_7': 7, 'margot kidder_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'original air date_5': 'original air date', '2_6': '2', 'celebrity_7': 'celebrity', 'margot kidder_8': 'margot kidder'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'original air date_5': [0], '2_6': [0], 'celebrity_7': [1], 'margot kidder_8': [2]} | ['total no', 'celebrity', 'director', 'original air date', 'viewers'] | [['1', 'shaun majumder', 'scott harper', '11 october 2007', 'n / a'], ['2', 'margot kidder', 'margaret slaght', '18 october 2007', 'n / a'], ['3', 'steven page', 'david langer', '25 october 2007', 'n / a'], ['4', 'sonja smits', 'karen pinker', '1 november 2007', 'n / a'], ['5', 'chantal kreviazuk', 'nadine schwartz', '8 november 2007', 'n / a'], ['6', 'major - general lewis mackenzie', 'richard martyn', '15 november 2007', 'n / a'], ['7', 'mary walsh', 'matt gallagher', '22 november 2007', 'n / a'], ['8', 'randy bachman', 'margaret slaght', '29 november 2007', 'n / a'], ['9', 'scott thompson', 'scott harper', '6 december 2007', 'n / a'], ['10', 'don cherry', 'richard martyn', '10 january 2008', 'n / a'], ['11', 'measha brueggergosman', 'karen pinker', '17 january 2008', 'n / a'], ['12', 'margaret trudeau', 'peter findlay', '24 january 2008', 'n / a']] |
list of longest - serving soap opera actors | https://en.wikipedia.org/wiki/List_of_longest-serving_soap_opera_actors | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18772558-9.html.csv | majority | of the longest serving soap opera actors , most of them are from un posto al sole . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'un posto al sole', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'soap opera', 'un posto al sole'], 'result': True, 'ind': 0, 'tointer': 'for the soap opera records of all rows , most of them fuzzily match to un posto al sole .', 'tostr': 'most_eq { all_rows ; soap opera ; un posto al sole } = true'} | most_eq { all_rows ; soap opera ; un posto al sole } = true | for the soap opera records of all rows , most of them fuzzily match to un posto al sole . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'soap opera_3': 3, 'un posto al sole_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'soap opera_3': 'soap opera', 'un posto al sole_4': 'un posto al sole'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'soap opera_3': [0], 'un posto al sole_4': [0]} | ['actor', 'character', 'soap opera', 'years', 'duration'] | [['patrizio rispo', 'raffaele giordano', 'un posto al sole', '1996 -', '18 years'], ['luisa amatucci', 'silvia graziani', 'un posto al sole', '1996 -', '18 years'], ['alberto rossi', 'michele saviani', 'un posto al sole', '1996 -', '18 years'], ['germano bellavia', 'guido del bue', 'un posto al sole', '1996 -', '18 years'], ['marzio honorato', 'renato poggi', 'un posto al sole', '1996 -', '18 years'], ['carmen scivittaro', 'teresa diacono', 'un posto al sole', '1998 -', '16 years'], ['peppe zarbo', 'franco boschi', 'un posto al sole', '1998 -', '16 years'], ['marina tagliaferri', 'giulia poggi', 'un posto al sole', '1996 - 2008 , 2011 -', '15 years'], ['claudia ruffo', 'angela poggi', 'un posto al sole', '1996 - 2007 , 2010 -', '15 years'], ['luca turco', 'niko poggi', 'un posto al sole', '1999 -', '15 years'], ['ilenia lazzarin', 'viola bruni', 'un posto al sole', '2001 -', '13 years'], ['marina giulia cavalli', 'ornella bruni', 'un posto al sole', '2001 -', '13 years'], ['riccardo polizzy carbonelli', 'roberto ferri', 'un posto al sole', '2001 -', '13 years'], ['elisabetta coraini', 'laura beccaria', 'centovetrine', '2001 -', '13 years'], ['pietro genuardi', 'ivan bettini', 'centovetrine', '2001 -', '13 years'], ['sergio troiano', 'valerio bettini', 'centovetrine', '2001 -', '13 years'], ['nina soldano', 'marina giordano', 'un posto al sole', '2003 -', '11 years'], ['delia boccardo', 'tilly nardi', 'incantesimo', '1998 - 2008', '10 years']] |
list of how it 's made episodes | https://en.wikipedia.org/wiki/List_of_How_It%27s_Made_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15187735-14.html.csv | comparative | the episode about fig cookies aired before the one about house paint . | {'row_1': '1', 'row_2': '10', 'col': '2', 'col_other': '5', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'segment b', 'fig cookies'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose segment b record fuzzily matches to fig cookies .', 'tostr': 'filter_eq { all_rows ; segment b ; fig cookies }'}, 'episode'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; segment b ; fig cookies } ; episode }', 'tointer': 'select the rows whose segment b record fuzzily matches to fig cookies . take the episode record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'segment b', 'house paint'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose segment b record fuzzily matches to house paint .', 'tostr': 'filter_eq { all_rows ; segment b ; house paint }'}, 'episode'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; segment b ; house paint } ; episode }', 'tointer': 'select the rows whose segment b record fuzzily matches to house paint . take the episode record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; segment b ; fig cookies } ; episode } ; hop { filter_eq { all_rows ; segment b ; house paint } ; episode } } = true', 'tointer': 'select the rows whose segment b record fuzzily matches to fig cookies . take the episode record of this row . select the rows whose segment b record fuzzily matches to house paint . take the episode record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; segment b ; fig cookies } ; episode } ; hop { filter_eq { all_rows ; segment b ; house paint } ; episode } } = true | select the rows whose segment b record fuzzily matches to fig cookies . take the episode record of this row . select the rows whose segment b record fuzzily matches to house paint . take the episode record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'segment b_7': 7, 'fig cookies_8': 8, 'episode_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'segment b_11': 11, 'house paint_12': 12, 'episode_13': 13} | {'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'segment b_7': 'segment b', 'fig cookies_8': 'fig cookies', 'episode_9': 'episode', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'segment b_11': 'segment b', 'house paint_12': 'house paint', 'episode_13': 'episode'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'segment b_7': [0], 'fig cookies_8': [0], 'episode_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'segment b_11': [1], 'house paint_12': [1], 'episode_13': [3]} | ['series ep', 'episode', 'netflix', 'segment a', 'segment b', 'segment c', 'segment d'] | [['14 - 01', '170', 's07e01', 'mini gp motorcycles', 'fig cookies', 'tool boxes', 'pipe bends'], ['14 - 02', '171', 's07e02', 'western revolver s replica', 'arc trainers', 'used - oil furnaces', 'vegetable peelers and s pizza cutter'], ['14 - 03', '172', 's07e03', 'metal s golf club', 's waffle', 'custom wires and s cable', 'train s wheel'], ['14 - 04', '173', 's07e04', 's sail', 's walnut', 'wheel immobilizers', 'honeycomb structural panels'], ['14 - 05', '174', 's07e05', 's surfboard', 's sticker', 'sandwich s cookie', 'concrete roofing s tile'], ['14 - 06', '175', 's07e06', 'ski goggles', 'tower cranes', 'porcelain s figurine', 's diesel engine'], ['14 - 07', '176', 's07e07', 'stuffed s olive', 's astrolabe', 's western saddle ( part 1 )', 's western saddle ( part 2 )'], ['14 - 08', '177', 's07e08', 'custom running shoes', 's axe', 'racing s kart', 's animatronic'], ['14 - 09', '178', 's07e09', 's headphone', 's diving regulator', 'reflector light bulbs ( part 1 )', 'reflector light bulbs ( part 2 )'], ['14 - 10', '179', 's07e10', 's fly fishing reel', 'house paint', 's weaving loom', 's ice maker'], ['14 - 11', '180', 's07e11', 's graphite pencil lead', 's clarinet', 's special effect ( part 1 )', 's special effect ( part 2 )'], ['14 - 12', '181', 's07e12', 's air boat', 's onion', '3d metal printing', 's curved cabinet door']] |
list of tallest buildings in the halifax regional municipality | https://en.wikipedia.org/wiki/List_of_tallest_buildings_in_the_Halifax_Regional_Municipality | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11339545-1.html.csv | superlative | fenwick tower has the most floors of any building in the list of tallest buildings in the halifax regional municipality . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'floors'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; floors }'}, 'building'], 'result': 'fenwick tower ( residential )', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; floors } ; building }'}, 'fenwick tower ( residential )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; floors } ; building } ; fenwick tower ( residential ) } = true', 'tointer': 'select the row whose floors record of all rows is maximum . the building record of this row is fenwick tower ( residential ) .'} | eq { hop { argmax { all_rows ; floors } ; building } ; fenwick tower ( residential ) } = true | select the row whose floors record of all rows is maximum . the building record of this row is fenwick tower ( residential ) . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'floors_5': 5, 'building_6': 6, 'fenwick tower (residential)_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'floors_5': 'floors', 'building_6': 'building', 'fenwick tower (residential)_7': 'fenwick tower ( residential )'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'floors_5': [0], 'building_6': [1], 'fenwick tower (residential)_7': [2]} | ['rank', 'building', 'height', 'floors', 'completed'] | [['1', 'fenwick tower ( residential )', '98 m ( 322ft )', '32', '1971'], ['2', "purdy 's wharf tower 2 ( office )", '88 m ( 289ft )', '22', '1990'], ['3', '1801 hollis street ( office )', '87 m ( 285ft )', '22', '1985'], ['4', 'barrington tower ( office )', '84 m ( 276ft )', '20', '1975'], ['5', 'cogswell tower ( office )', '79 m ( 259ft )', '20', '1975'], ['6', 'maritime centre ( office )', '78 m ( 256ft )', '21', '1974'], ['7', 'queen square ( office )', '75 m ( 246ft )', '19', '1975'], ['8', "purdy 's wharf tower 1 ( office )", '74 m ( 243ft )', '18', '1985'], ['9', 'bank of montreal building ( office )', '73 m ( 240ft )', '18', '1971'], ['10', 'td tower ( office )', '73 m ( 240ft )', '18', '1974'], ['11', 'duke tower ( office )', '71 m ( 233ft )', '16', '1970'], ['12', 'founders square ( office )', '71 m ( 233ft )', '15', '1970'], ['13', 'tupper building ( educational )', '70 m ( 233ft )', '16', '1967'], ['14', 'park victoria ( residential )', '70 m ( 233ft )', '21', '1969'], ['15', 'summer gardens ( residential )', '70 m ( 233ft )', '21', '1990'], ['16', 'loyola residence tower ( residential )', '67 m ( 220ft )', '22', '1971'], ['17', 'metropolitan place ( office )', '67 m ( 218ft )', '16', '1987'], ['18', 'bank of commerce ( office )', '66 m ( 217ft )', '16', '1977'], ['19', 'the trillium ( residential )', '65 m ( 213ft )', '19', '2011']] |
wru division two west | https://en.wikipedia.org/wiki/WRU_Division_Two_West | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12828723-4.html.csv | superlative | gorseinon rfc had the highest number of points against among clubs in the wru division two west . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '13', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'points against'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; points against }'}, 'club'], 'result': 'gorseinon rfc', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; points against } ; club }'}, 'gorseinon rfc'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; points against } ; club } ; gorseinon rfc } = true', 'tointer': 'select the row whose points against record of all rows is maximum . the club record of this row is gorseinon rfc .'} | eq { hop { argmax { all_rows ; points against } ; club } ; gorseinon rfc } = true | select the row whose points against record of all rows is maximum . the club record of this row is gorseinon rfc . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'points against_5': 5, 'club_6': 6, 'gorseinon rfc_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'points against_5': 'points against', 'club_6': 'club', 'gorseinon rfc_7': 'gorseinon rfc'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'points against_5': [0], 'club_6': [1], 'gorseinon rfc_7': [2]} | ['club', 'played', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus', 'losing bonus', 'points'] | [['club', 'played', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus', 'losing bonus', 'points'], ['bridgend athletic rfc', '22', '0', '6', '523', '303', '68', '31', '10', '4', '78'], ['builth wells rfc', '22', '0', '5', '473', '305', '57', '29', '7', '2', '77'], ['kidwelly rfc', '22', '1', '7', '532', '386', '63', '45', '5', '3', '66'], ['loughor rfc', '22', '1', '8', '532', '388', '69', '43', '9', '1', '64'], ['ammanford rfc', '22', '0', '9', '447', '394', '58', '51', '6', '4', '62'], ['waunarlwydd rfc', '22', '2', '8', '504', '439', '57', '55', '6', '3', '61'], ['pencoed rfc', '22', '0', '9', '425', '328', '53', '36', '4', '4', '60'], ['bp rfc', '22', '1', '12', '367', '358', '39', '43', '2', '7', '47'], ['mumbles rfc', '22', '2', '12', '373', '450', '50', '56', '4', '4', '44'], ['cwmavon rfc', '22', '2', '14', '332', '515', '39', '66', '3', '5', '36'], ['penclawdd rfc', '22', '1', '17', '263', '520', '28', '68', '1', '3', '22'], ['gorseinon rfc', '22', '0', '20', '340', '725', '48', '106', '3', '4', '15']] |
1959 cleveland browns season | https://en.wikipedia.org/wiki/1959_Cleveland_Browns_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10651674-1.html.csv | ordinal | in the first game of the 1959 cleveland browns season , the fourth game attracted 55883 fans to the arena . | {'scope': 'all', 'row': '4', 'col': '1', 'order': '4', 'col_other': '5', 'max_or_min': 'min_to_max', 'value_mentioned': 'yes', 'subset': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'nth_min', 'args': ['all_rows', 'week', '4'], 'result': '4', 'ind': 0, 'tostr': 'nth_min { all_rows ; week ; 4 }', 'tointer': 'the 4th minimum week record of all rows is 4 .'}, '4'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_min { all_rows ; week ; 4 } ; 4 }', 'tointer': 'the 4th minimum week record of all rows is 4 .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'week', '4'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; week ; 4 }'}, 'attendance'], 'result': '55883', 'ind': 3, 'tostr': 'hop { nth_argmin { all_rows ; week ; 4 } ; attendance }'}, '55883'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { nth_argmin { all_rows ; week ; 4 } ; attendance } ; 55883 }', 'tointer': 'the attendance record of the row with 4th minimum week record is 55883 .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { nth_min { all_rows ; week ; 4 } ; 4 } ; eq { hop { nth_argmin { all_rows ; week ; 4 } ; attendance } ; 55883 } } = true', 'tointer': 'the 4th minimum week record of all rows is 4 . the attendance record of the row with 4th minimum week record is 55883 .'} | and { eq { nth_min { all_rows ; week ; 4 } ; 4 } ; eq { hop { nth_argmin { all_rows ; week ; 4 } ; attendance } ; 55883 } } = true | the 4th minimum week record of all rows is 4 . the attendance record of the row with 4th minimum week record is 55883 . | 6 | 6 | {'and_5': 5, 'result_6': 6, 'eq_1': 1, 'nth_min_0': 0, 'all_rows_7': 7, 'week_8': 8, '4_9': 9, '4_10': 10, 'eq_4': 4, 'num_hop_3': 3, 'nth_argmin_2': 2, 'all_rows_11': 11, 'week_12': 12, '4_13': 13, 'attendance_14': 14, '55883_15': 15} | {'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'nth_min_0': 'nth_min', 'all_rows_7': 'all_rows', 'week_8': 'week', '4_9': '4', '4_10': '4', 'eq_4': 'eq', 'num_hop_3': 'num_hop', 'nth_argmin_2': 'nth_argmin', 'all_rows_11': 'all_rows', 'week_12': 'week', '4_13': '4', 'attendance_14': 'attendance', '55883_15': '55883'} | {'and_5': [6], 'result_6': [], 'eq_1': [5], 'nth_min_0': [1], 'all_rows_7': [0], 'week_8': [0], '4_9': [0], '4_10': [1], 'eq_4': [5], 'num_hop_3': [4], 'nth_argmin_2': [3], 'all_rows_11': [2], 'week_12': [2], '4_13': [2], 'attendance_14': [3], '55883_15': [4]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'august 12 , 1959', 'pittsburgh steelers', 'l 34 - 20', '27432'], ['2', 'august 22 , 1959', 'detroit lions at akron', 'l 9 - 3', '22654'], ['3', 'august 30 , 1959', 'san francisco 49ers', 'l 17 - 14', '24737'], ['4', 'september 5 , 1959', 'los angeles rams', 'w 27 - 24', '55883'], ['5', 'september 13 , 1959', 'detroit lions', 'l 31 - 28', '33435'], ['6', 'september 19 , 1959', 'chicago bears', 'w 33 - 31', '25316']] |
smallville ( season 10 ) | https://en.wikipedia.org/wiki/Smallville_%28season_10%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26464364-1.html.csv | ordinal | the season 10 premier episode of smallville had the second highest viewer count . | {'row': '1', 'col': '8', 'order': '2', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'us viewers ( million )', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; us viewers ( million ) ; 2 }'}, '-'], 'result': '1', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; us viewers ( million ) ; 2 } ; - }'}, '1'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; us viewers ( million ) ; 2 } ; - } ; 1 } = true', 'tointer': 'select the row whose us viewers ( million ) record of all rows is 2nd maximum . the - record of this row is 1 .'} | eq { hop { nth_argmax { all_rows ; us viewers ( million ) ; 2 } ; - } ; 1 } = true | select the row whose us viewers ( million ) record of all rows is 2nd maximum . the - record of this row is 1 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'us viewers (million)_5': 5, '2_6': 6, '-_7': 7, '1_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'us viewers (million)_5': 'us viewers ( million )', '2_6': '2', '-_7': '-', '1_8': '1'} | {'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'us viewers (million)_5': [0], '2_6': [0], '-_7': [1], '1_8': [2]} | ['no', '-', 'title', 'directed by', 'written by', 'us air date', 'production code', 'us viewers ( million )'] | [['196', '1', 'lazarus', 'kevin g fair', 'don whitehead & holly henderson', 'september 24 , 2010', '3x6001', '2.98'], ['197', '2', 'shield', 'glen winter', 'jordan hawley', 'october 1 , 2010', '3x6002', '2.38'], ['198', '3', 'supergirl', 'mairzee almas', 'anne cofell saunders', 'october 8 , 2010', '3x6003', '2.30'], ['199', '4', 'homecoming', 'jeannot szwarc', 'brian peterson & kelly souders', 'october 15 , 2010', '3x6004', '3.19'], ['200', '5', 'isis', 'james marshall', 'genevieve sparling', 'october 22 , 2010', '3x6005', '2.60'], ['201', '6', 'harvest', 'turi meyer', 'al septien & turi meyer', 'october 29 , 2010', '3x6007', '2.96'], ['202', '7', 'ambush', 'christopher petry', 'don whitehead & holly henderson', 'november 5 , 2010', '3x6006', '2.63'], ['203', '8', 'abandoned', 'kevin g fair', 'drew landis & julia swift', 'november 12 , 2010', '3x6008', '2.90'], ['204', '9', 'patriot', 'tom welling', 'john chisholm', 'november 19 , 2010', '3x6009', '2.60'], ['205', '10', 'luthor', 'kelly souders', 'bryan q miller', 'december 3 , 2010', '3x6010', '2.76'], ['206', '11', 'icarus', 'mairzee almas', 'genevieve sparling', 'december 10 , 2010', '3x6011', '2.55'], ['207', '12', 'collateral', 'morgan beggs', 'jordan hawley', 'february 4 , 2011', '3x6012', '2.37'], ['208', '13', 'beacon', 'mike rohl', 'don whitehead & holly henderson', 'february 11 , 2011', '3x6013', '2.32'], ['209', '14', 'masquerade', 'tim scanlan', 'bryan q miller', 'february 18 , 2011', '3x6014', '2.22'], ['210', '15', 'fortune', 'christopher petry', 'anne coffell saunders', 'february 25 , 2011', '3x6015', '2.56'], ['211', '16', 'scion', 'al septien', 'al septien & turi meyer', 'march 4 , 2011', '3x6016', '2.18'], ['212', '17', 'kent', 'jeannot szwarc', 'brian peterson & kelly souders', 'april 15 , 2011', '3x6018', '2.37'], ['213', '18', 'booster', 'tom welling', 'geoff johns', 'april 22 , 2011', '3x6017', '2.35'], ['214', '19', 'dominion', 'justin hartley', 'john chisholm', 'april 29 , 2011', '3x6021', '1.99']] |
amino acid | https://en.wikipedia.org/wiki/Amino_acid | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1207-4.html.csv | aggregation | the standard amino acids have an average hydropathy index of 2.6 . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '2.6', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'hydropathy index'], 'result': '2.6', 'ind': 0, 'tostr': 'avg { all_rows ; hydropathy index }'}, '2.6'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; hydropathy index } ; 2.6 } = true', 'tointer': 'the average of the hydropathy index record of all rows is 2.6 .'} | round_eq { avg { all_rows ; hydropathy index } ; 2.6 } = true | the average of the hydropathy index record of all rows is 2.6 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'hydropathy index_4': 4, '2.6_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'hydropathy index_4': 'hydropathy index', '2.6_5': '2.6'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'hydropathy index_4': [0], '2.6_5': [1]} | ['amino acid', '3 - letter', '1 - letter', 'side - chain polarity', 'side - chain charge ( ph 7.4 )', 'hydropathy index'] | [['alanine', 'ala', 'a', 'nonpolar', 'neutral', '1.8'], ['arginine', 'arg', 'r', 'basic polar', 'positive', '4.5'], ['asparagine', 'asn', 'n', 'polar', 'neutral', '3.5'], ['aspartic acid', 'asp', 'd', 'acidic polar', 'negative', '3.5'], ['cysteine', 'cys', 'c', 'nonpolar', 'neutral', '2.5'], ['glutamic acid', 'glu', 'e', 'acidic polar', 'negative', '3.5'], ['glutamine', 'gln', 'q', 'polar', 'neutral', '3.5'], ['glycine', 'gly', 'g', 'nonpolar', 'neutral', '0.4'], ['histidine', 'his', 'h', 'basic polar', 'positive ( 10 % ) neutral ( 90 % )', '3.2'], ['isoleucine', 'ile', 'i', 'nonpolar', 'neutral', '4.5'], ['leucine', 'leu', 'l', 'nonpolar', 'neutral', '3.8'], ['lysine', 'lys', 'k', 'basic polar', 'positive', '3.9'], ['methionine', 'met', 'm', 'nonpolar', 'neutral', '1.9'], ['phenylalanine', 'phe', 'f', 'nonpolar', 'neutral', '2.8'], ['proline', 'pro', 'p', 'nonpolar', 'neutral', '1.6'], ['serine', 'ser', 's', 'polar', 'neutral', '0.8'], ['threonine', 'thr', 't', 'polar', 'neutral', '0.7'], ['tryptophan', 'trp', 'w', 'nonpolar', 'neutral', '0.9'], ['tyrosine', 'tyr', 'y', 'polar', 'neutral', '1.3'], ['valine', 'val', 'v', 'nonpolar', 'neutral', '4.2']] |
1903 in paleontology | https://en.wikipedia.org/wiki/1903_in_paleontology | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15689683-1.html.csv | majority | in 1903 paleontology , most of the recordings were in colorado . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'colorado', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'unit', 'colorado'], 'result': True, 'ind': 0, 'tointer': 'for the unit records of all rows , most of them fuzzily match to colorado .', 'tostr': 'most_eq { all_rows ; unit ; colorado } = true'} | most_eq { all_rows ; unit ; colorado } = true | for the unit records of all rows , most of them fuzzily match to colorado . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'unit_3': 3, 'colorado_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'unit_3': 'unit', 'colorado_4': 'colorado'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'unit_3': [0], 'colorado_4': [0]} | ['name', 'novelty', 'status', 'authors', 'unit', 'location'] | [['brachiosaurus', 'gen et sp', 'valid', 'riggs', 'morrison formation , colorado', 'usa'], ['haplocanthosaurus', 'gen et sp', 'valid , nomen conservandum', 'hatcher', 'morrison formation , colorado', 'usa'], ['haplocanthus', 'gen et sp', 'nomen oblitum', 'hatcher', 'morrison formation , colorado', 'usa'], ['ornitholestes', 'gen et sp', 'valid', 'osborn', 'morrison formation , wyoming', 'usa'], ['telmatosaurus', 'gen', 'valid', 'nopcsa', 'sãnpetru formation , transylvania', 'romania']] |
ufc 94 | https://en.wikipedia.org/wiki/UFC_94 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16023995-1.html.csv | aggregation | across all cards , 26 total rounds were fought in ufc 94 . | {'scope': 'all', 'col': '3', 'type': 'sum', 'result': '26', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'round'], 'result': '26', 'ind': 0, 'tostr': 'sum { all_rows ; round }'}, '26'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; round } ; 26 } = true', 'tointer': 'the sum of the round record of all rows is 26 .'} | round_eq { sum { all_rows ; round } ; 26 } = true | the sum of the round record of all rows is 26 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'round_4': 4, '26_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'round_4': 'round', '26_5': '26'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'round_4': [0], '26_5': [1]} | ['card', 'weight class', 'round', 'time', 'method'] | [['preliminary', 'welterweight', '3', '5:00', 'decision ( split )'], ['preliminary', 'light heavyweight', '3', '5:00', 'decision ( split )'], ['preliminary', 'lightweight', '3', '5:00', 'decision ( unanimous )'], ['preliminary', 'welterweight', '3', '5:00', 'decision ( unanimous )'], ['main', 'lightweight', '3', '5:00', 'decision ( split )'], ['main', 'welterweight', '3', '5:00', 'no contest'], ['main', 'light heavyweight', '3', '5:00', 'decision ( unanimous )'], ['main', 'light heavyweight', '1', '4:59', 'ko ( punch )'], ['main', 'welterweight', '4', '5:00', 'tko ( doctor stoppage )']] |
narratives of empire | https://en.wikipedia.org/wiki/Narratives_of_Empire | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11251694-1.html.csv | superlative | of the narratives of empire , the most recent one published was the golden age . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '7', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'published'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; published }'}, 'title'], 'result': 'the golden age', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; published } ; title }'}, 'the golden age'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; published } ; title } ; the golden age } = true', 'tointer': 'select the row whose published record of all rows is maximum . the title record of this row is the golden age .'} | eq { hop { argmax { all_rows ; published } ; title } ; the golden age } = true | select the row whose published record of all rows is maximum . the title record of this row is the golden age . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'published_5': 5, 'title_6': 6, 'the golden age_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'published_5': 'published', 'title_6': 'title', 'the golden age_7': 'the golden age'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'published_5': [0], 'title_6': [1], 'the golden age_7': [2]} | ['order', 'title', 'story timeline', 'published', 'in order of publication'] | [['1', 'burr', '1775 - 1808 , 1833 - 1836 , 1840', '1973', 'second'], ['2', 'lincoln', '1861 - 1865', '1984', 'fourth'], ['3', '1876', '1875 - 1877', '1976', 'third'], ['4', 'empire', '1898 - 1907', '1987', 'fifth'], ['5', 'hollywood', '1917 - 1923', '1990', 'sixth'], ['6', 'washington , dc', '1937 - 1952', '1967', 'first'], ['7', 'the golden age', '1939 - 1954 , 2000', '2000', 'seventh']] |
1952 vfl season | https://en.wikipedia.org/wiki/1952_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10750694-19.html.csv | ordinal | the game in punt road oval had the second highest crowd in the 1952 season . | {'row': '5', 'col': '6', 'order': '2', 'col_other': '5', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'crowd', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; crowd ; 2 }'}, 'venue'], 'result': 'punt road oval', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; crowd ; 2 } ; venue }'}, 'punt road oval'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; punt road oval } = true', 'tointer': 'select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is punt road oval .'} | eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; punt road oval } = true | select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is punt road oval . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'crowd_5': 5, '2_6': 6, 'venue_7': 7, 'punt road oval_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'crowd_5': 'crowd', '2_6': '2', 'venue_7': 'venue', 'punt road oval_8': 'punt road oval'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'crowd_5': [0], '2_6': [0], 'venue_7': [1], 'punt road oval_8': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['hawthorn', '8.11 ( 59 )', 'north melbourne', '12.10 ( 82 )', 'glenferrie oval', '6000', '30 august 1952'], ['footscray', '13.13 ( 91 )', 'south melbourne', '8.13 ( 61 )', 'western oval', '20723', '30 august 1952'], ['collingwood', '13.14 ( 92 )', 'melbourne', '10.11 ( 71 )', 'victoria park', '18753', '30 august 1952'], ['st kilda', '10.12 ( 72 )', 'fitzroy', '8.18 ( 66 )', 'junction oval', '9000', '30 august 1952'], ['richmond', '15.11 ( 101 )', 'essendon', '11.10 ( 76 )', 'punt road oval', '28000', '30 august 1952'], ['geelong', '10.17 ( 77 )', 'carlton', '3.14 ( 32 )', 'kardinia park', '49107', '30 august 1952']] |
2008 in paleontology | https://en.wikipedia.org/wiki/2008_in_paleontology | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15688561-8.html.csv | unique | in 2008 in paleontology , when the location is china , the only time the author was yuan was for didactylornis . | {'scope': 'subset', 'row': '2', 'col': '3', 'col_other': '1,4', 'criterion': 'equal', 'value': 'yuan', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'china'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'china'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; location ; china }', 'tointer': 'select the rows whose location record fuzzily matches to china .'}, 'authors', 'yuan'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose location record fuzzily matches to china . among these rows , select the rows whose authors record fuzzily matches to yuan .', 'tostr': 'filter_eq { filter_eq { all_rows ; location ; china } ; authors ; yuan }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; location ; china } ; authors ; yuan } }', 'tointer': 'select the rows whose location record fuzzily matches to china . among these rows , select the rows whose authors record fuzzily matches to yuan . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'china'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; location ; china }', 'tointer': 'select the rows whose location record fuzzily matches to china .'}, 'authors', 'yuan'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose location record fuzzily matches to china . among these rows , select the rows whose authors record fuzzily matches to yuan .', 'tostr': 'filter_eq { filter_eq { all_rows ; location ; china } ; authors ; yuan }'}, 'name'], 'result': 'didactylornis', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; location ; china } ; authors ; yuan } ; name }'}, 'didactylornis'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; location ; china } ; authors ; yuan } ; name } ; didactylornis }', 'tointer': 'the name record of this unqiue row is didactylornis .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; location ; china } ; authors ; yuan } } ; eq { hop { filter_eq { filter_eq { all_rows ; location ; china } ; authors ; yuan } ; name } ; didactylornis } } = true', 'tointer': 'select the rows whose location record fuzzily matches to china . among these rows , select the rows whose authors record fuzzily matches to yuan . there is only one such row in the table . the name record of this unqiue row is didactylornis .'} | and { only { filter_eq { filter_eq { all_rows ; location ; china } ; authors ; yuan } } ; eq { hop { filter_eq { filter_eq { all_rows ; location ; china } ; authors ; yuan } ; name } ; didactylornis } } = true | select the rows whose location record fuzzily matches to china . among these rows , select the rows whose authors record fuzzily matches to yuan . there is only one such row in the table . the name record of this unqiue row is didactylornis . | 8 | 6 | {'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'location_8': 8, 'china_9': 9, 'authors_10': 10, 'yuan_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'name_12': 12, 'didactylornis_13': 13} | {'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'location_8': 'location', 'china_9': 'china', 'authors_10': 'authors', 'yuan_11': 'yuan', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'name_12': 'name', 'didactylornis_13': 'didactylornis'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'location_8': [0], 'china_9': [0], 'authors_10': [1], 'yuan_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'name_12': [3], 'didactylornis_13': [4]} | ['name', 'status', 'authors', 'location', 'notes'] | [['caracara tellustris', 'valid', 'olson', 'jamaica', 'a species of caracara'], ['didactylornis', 'valid', 'yuan', 'china', 'basal n pygostylia'], ['enantiophoenix', 'valid', 'cau arduini', 'lebanon', 'an enantiornithine'], ['eoconfuciusornis', 'valid', 'zhang zhou benton', 'china', 'primitive confuciusornithid'], ['pengornis', 'valid', 'zhou clarke zhang', 'china', 'an enantiornithine'], ['zhongornis', 'valid', "gao chiappe meng o'connor wang cheng liu", 'china', 'basal bird']] |
1983 nhl entry draft | https://en.wikipedia.org/wiki/1983_NHL_Entry_Draft | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2679061-9.html.csv | majority | the majority of players selected in picks 163 to 182 of the 1983 nhl draft were canadian , . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'canada', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'nationality', 'canada'], 'result': True, 'ind': 0, 'tointer': 'for the nationality records of all rows , most of them fuzzily match to canada .', 'tostr': 'most_eq { all_rows ; nationality ; canada } = true'} | most_eq { all_rows ; nationality ; canada } = true | for the nationality records of all rows , most of them fuzzily match to canada . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'nationality_3': 3, 'canada_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'nationality_3': 'nationality', 'canada_4': 'canada'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'nationality_3': [0], 'canada_4': [0]} | ['pick', 'player', 'position', 'nationality', 'nhl team', 'college / junior / club team'] | [['163', 'marty ketola', 'right wing', 'united states', 'pittsburgh penguins', 'cloquet high school ( ushs - mn )'], ['164', 'bill fordy', 'left wing', 'canada', 'hartford whalers', 'guelph platers ( ohl )'], ['165', 'jay octeau', 'defence', 'united states', 'new jersey devils', 'mount st charles academy ( ushs - ri )'], ['166', 'dave sikorski', 'defence', 'united states', 'detroit red wings', 'cornwall royals ( ohl )'], ['167', 'bruce fishback', 'centre', 'united states', 'los angeles kings', 'white bear lake high school ( ushs - mn )'], ['168', 'cliff abrecht', 'defence', 'canada', 'toronto maple leafs', 'princeton university ( ecac )'], ['169', 'todd flichel', 'defence', 'canada', 'winnipeg jets', 'gloucester rangers ( cojhl )'], ['170', 'allan measures', 'defence', 'canada', 'vancouver canucks', 'calgary wranglers ( whl )'], ['171', 'rob kivell', 'defence', 'canada', 'calgary flames', 'victoria cougars ( whl )'], ['172', 'wayne groulx', 'centre', 'canada', 'quebec nordiques', 'sault ste marie greyhounds ( ohl )'], ['173', 'paul jerrard', 'right wing', 'canada', 'new york rangers', 'notre dame hounds ( sjhl )'], ['174', 'tim hoover', 'defence', 'canada', 'buffalo sabres', 'sault ste marie greyhounds ( ohl )'], ['175', 'dave cowan', 'left wing', 'united states', 'washington capitals', 'washburn high school ( ushs - mn )'], ['176', 'paul pulis', 'right wing', 'united states', 'minnesota north stars', 'hibbing high school ( ushs - mn )'], ['177', 'kevin vescio', 'defence', 'canada', 'new york islanders', 'north bay centennials ( ohl )'], ['178', 'grant mckay', 'defence', 'canada', 'montreal canadiens', 'university of calgary ( ciau )'], ['179', 'brian noonan', 'centre', 'united states', 'chicago black hawks', 'archbishop williams high school ( ushs - ma )'], ['180', 'dave roach', 'goaltender', 'canada', 'edmonton oilers', 'new westminster royals ( bcjhl )'], ['181', 'robbie nichols', 'left wing', 'canada', 'philadelphia flyers', 'kitchener rangers ( ohl )'], ['182', 'harri laurila', 'defence', 'finland', 'boston bruins', 'lahti ( finland )']] |
german submarine u - 137 ( 1940 ) | https://en.wikipedia.org/wiki/German_submarine_U-137_%281940%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18914307-1.html.csv | superlative | the highest number of deaths in a german submarine u-137 was in the ship named manchester brigade . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '2', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'deaths'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; deaths }'}, 'ship name'], 'result': 'manchester brigade', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; deaths } ; ship name }'}, 'manchester brigade'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; deaths } ; ship name } ; manchester brigade } = true', 'tointer': 'select the row whose deaths record of all rows is maximum . the ship name record of this row is manchester brigade .'} | eq { hop { argmax { all_rows ; deaths } ; ship name } ; manchester brigade } = true | select the row whose deaths record of all rows is maximum . the ship name record of this row is manchester brigade . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'deaths_5': 5, 'ship name_6': 6, 'manchester brigade_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'deaths_5': 'deaths', 'ship name_6': 'ship name', 'manchester brigade_7': 'manchester brigade'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'deaths_5': [0], 'ship name_6': [1], 'manchester brigade_7': [2]} | ['date', 'ship name', 'flag', 'tonnage ( grt )', 'fate', 'deaths'] | [['26 september 1940', 'ashantian', 'great britain', '4917', 'damaged', '4'], ['26 september 1940', 'manchester brigade', 'great britain', '6042', 'sunk', '56'], ['26 september 1940', 'stratford', 'great britain', '4753', 'sunk', '2'], ['14 october 1940', 'hms cheshire', 'great britain', '10552', 'damaged', '0'], ['13 november 1940', 'cape st andrew', 'great britain', '5094', 'sunk', '15'], ['16 november 1940', 'planter', 'great britain', '5887', 'sunk', '13'], ['17 november 1940', 'saint germain', 'great britain', '1044', 'sunk', '0'], ['17 november 1940', 'veronica', 'sweden', '1316', 'sunk', '17']] |
1996 senior pga tour | https://en.wikipedia.org/wiki/1996_Senior_PGA_Tour | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11621873-3.html.csv | unique | of the top-ranked players in the 1996 senior pga tour , only one came from japan . | {'scope': 'all', 'row': '4', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': 'japan', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'japan'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to japan .', 'tostr': 'filter_eq { all_rows ; country ; japan }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; country ; japan } }', 'tointer': 'select the rows whose country record fuzzily matches to japan . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'japan'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to japan .', 'tostr': 'filter_eq { all_rows ; country ; japan }'}, 'rank'], 'result': '4', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; country ; japan } ; rank }'}, '4'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; country ; japan } ; rank } ; 4 }', 'tointer': 'the rank record of this unqiue row is 4 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; country ; japan } } ; eq { hop { filter_eq { all_rows ; country ; japan } ; rank } ; 4 } } = true', 'tointer': 'select the rows whose country record fuzzily matches to japan . there is only one such row in the table . the rank record of this unqiue row is 4 .'} | and { only { filter_eq { all_rows ; country ; japan } } ; eq { hop { filter_eq { all_rows ; country ; japan } ; rank } ; 4 } } = true | select the rows whose country record fuzzily matches to japan . there is only one such row in the table . the rank record of this unqiue row is 4 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'country_7': 7, 'japan_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'rank_9': 9, '4_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'country_7': 'country', 'japan_8': 'japan', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'rank_9': 'rank', '4_10': '4'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'country_7': [0], 'japan_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'rank_9': [2], '4_10': [3]} | ['rank', 'player', 'country', 'earnings', 'events', 'wins'] | [['1', 'jim colbert', 'united states', '1627890', '32', '5'], ['2', 'hale irwin', 'united states', '1615769', '23', '2'], ['3', 'john bland', 'south africa', '1357987', '35', '4'], ['4', 'isao aoki', 'japan', '1162581', '26', '2'], ['5', 'dave stockton', 'united states', '1117685', '29', '2']] |
television in italy | https://en.wikipedia.org/wiki/Television_in_Italy | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15887683-16.html.csv | count | 2 of the tv stations of italy have " telemarket " in their title . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'telemarket', 'result': '2', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'television service', 'telemarket'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose television service record fuzzily matches to telemarket .', 'tostr': 'filter_eq { all_rows ; television service ; telemarket }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; television service ; telemarket } }', 'tointer': 'select the rows whose television service record fuzzily matches to telemarket . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; television service ; telemarket } } ; 2 } = true', 'tointer': 'select the rows whose television service record fuzzily matches to telemarket . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; television service ; telemarket } } ; 2 } = true | select the rows whose television service record fuzzily matches to telemarket . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'television service_5': 5, 'telemarket_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'television service_5': 'television service', 'telemarket_6': 'telemarket', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'television service_5': [0], 'telemarket_6': [0], '2_7': [2]} | ['n degree', 'television service', 'country', 'language', 'content', 'dar', 'hdtv', 'package / option'] | [['861', 'telemarket', 'italy', 'italian', 'televendita', '4:3', 'no', 'no ( fta )'], ['862', 'noello sat', 'italy', 'italian', 'televendita', '4:3', 'no', 'no ( fta )'], ['863', 'elite shopping tv', 'italy', 'italian', 'televendita', '4:3', 'no', 'no ( fta )'], ['864', 'juwelo', 'italy', 'italian', 'televendita', '4:3', 'no', 'no ( fta )'], ['865', 'diprè tv', 'italy', 'italian', 'arte', '4:3', 'no', 'no ( fta )'], ['866', 'telemarket for you', 'italy', 'italian', 'televendita', '4:3', 'no', 'no ( fta )'], ['867', 'la sorgente sat 1', 'italy', 'italian', 'televendita', '4:3', 'no', 'no ( fta )'], ['868', 'la sorgente sat 2', 'italy', 'italian', 'televendita', '4:3', 'no', 'no ( fta )'], ['869', 'la sorgente sat 3', 'italy', 'italian', 'televendita', '4:3', 'no', 'no ( fta )']] |
2002 mls superdraft | https://en.wikipedia.org/wiki/2002_MLS_SuperDraft | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1026919-3.html.csv | count | in the 2002 mls superdraft , for players in the m position , 3 were higher picks than 27 . | {'scope': 'subset', 'criterion': 'greater_than', 'value': '27', 'result': '3', 'col': '1', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'm'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', 'm'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; position ; m }', 'tointer': 'select the rows whose position record fuzzily matches to m .'}, 'pick', '27'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose position record fuzzily matches to m . among these rows , select the rows whose pick record is greater than 27 .', 'tostr': 'filter_greater { filter_eq { all_rows ; position ; m } ; pick ; 27 }'}], 'result': '3', 'ind': 2, 'tostr': 'count { filter_greater { filter_eq { all_rows ; position ; m } ; pick ; 27 } }', 'tointer': 'select the rows whose position record fuzzily matches to m . among these rows , select the rows whose pick record is greater than 27 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_greater { filter_eq { all_rows ; position ; m } ; pick ; 27 } } ; 3 } = true', 'tointer': 'select the rows whose position record fuzzily matches to m . among these rows , select the rows whose pick record is greater than 27 . the number of such rows is 3 .'} | eq { count { filter_greater { filter_eq { all_rows ; position ; m } ; pick ; 27 } } ; 3 } = true | select the rows whose position record fuzzily matches to m . among these rows , select the rows whose pick record is greater than 27 . the number of such rows is 3 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_greater_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'position_6': 6, 'm_7': 7, 'pick_8': 8, '27_9': 9, '3_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_greater_1': 'filter_greater', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'position_6': 'position', 'm_7': 'm', 'pick_8': 'pick', '27_9': '27', '3_10': '3'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_greater_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'position_6': [0], 'm_7': [0], 'pick_8': [1], '27_9': [1], '3_10': [3]} | ['pick', 'mls team', 'player', 'position', 'affiliation'] | [['26', 'chicago fire', 'steve totten', 'm', 'university of virginia'], ['27', 'los angeles galaxy', 'alejandro moreno', 'f', 'unc - greensboro'], ['28', 'colorado rapids', 'bryn ritchie', 'd', 'university of washington'], ['29', 'colorado rapids', 'daniel alvarez', 'm', 'furman university'], ['30', 'metrostars', 'sam forko', 'd', 'university of connecticut'], ['31', 'dc united', 'mohammed fahim', 'f', 'southern methodist university'], ['32', 'kansas city wizards', "o'neil peart", 'f', 'long island rough riders ( a - league )'], ['33', 'san jose earthquakes', 'chris roner', 'm', 'university of california , berkeley'], ['34', 'colorado rapids', 'matt moses', 'm', 'stanford university'], ['35', 'columbus crew', 'john barry nusum', 'f', 'furman university'], ['36', 'chicago fire', 'dipsy selolwane', 'f', 'st louis university'], ['37', 'kansas city wizards', 'chris brunt', 'd', 'southwest missouri state'], ['38', 'los angeles galaxy', 'cory gibbs', 'd', 'brown university'], ['39', 'san jose earthquakes', 'kevin sakuda', 'd', 'duke university']] |
royal canadian mint numismatic coins ( 2000s ) | https://en.wikipedia.org/wiki/Royal_Canadian_Mint_numismatic_coins_%282000s%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11916083-39.html.csv | superlative | the coin with highest mintage of the royal canadian mint numismatic coins in the 2000s received the theme steam buggy . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'mintage'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; mintage }'}, 'theme'], 'result': 'steam buggy', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; mintage } ; theme }'}, 'steam buggy'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; mintage } ; theme } ; steam buggy } = true', 'tointer': 'select the row whose mintage record of all rows is maximum . the theme record of this row is steam buggy .'} | eq { hop { argmax { all_rows ; mintage } ; theme } ; steam buggy } = true | select the row whose mintage record of all rows is maximum . the theme record of this row is steam buggy . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'mintage_5': 5, 'theme_6': 6, 'steam buggy_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'mintage_5': 'mintage', 'theme_6': 'theme', 'steam buggy_7': 'steam buggy'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'mintage_5': [0], 'theme_6': [1], 'steam buggy_7': [2]} | ['year', 'theme', 'artist', 'mintage', 'issue price'] | [['2000', 'steam buggy', 'john mardon', '44367', '59.95'], ['2000', 'the bluenose', 'j franklin wright', 'included in steam buggy', '59.95'], ['2000', 'the toronto', 'john mardon', 'included in steam buggy', '59.95'], ['2001', 'the russell light four', 'john mardon', '41828', '59.95'], ['2001', 'the marco polo', 'j franklin wright', 'included in the russell', '59.95'], ['2001', 'the scotia', 'don curley', 'included in the russell', '59.95'], ['2002', 'the gray - dort', 'john mardon', '35944', '59.95'], ['2002', 'the william lawrence', 'bonnie ross', 'included in the gray - dort', '59.95'], ['2002', 'd - 10 locomotive', 'dan fell', 'included in the gray - dort', '59.95'], ['2003', 'hmcs bras dor', 'don curley', '31997', '59.95'], ['2003', 'cnr fa - 1 diesel electric', 'john mardon', 'included in hmcs bras dor', '59.95'], ['2003', 'bricklin sv - 1', 'brian hughes', 'included in hmcs bras dor', '59.95']] |
2002 grand national | https://en.wikipedia.org/wiki/2002_Grand_National | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25429986-1.html.csv | count | in 2002 grand national , one of age 8 has sp 20/1 . | {'scope': 'subset', 'criterion': 'equal', 'value': '20 / 1', 'result': '1', 'col': '7', 'subset': {'col': '5', 'criterion': 'equal', 'value': '8'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'age', '8'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; age ; 8 }', 'tointer': 'select the rows whose age record is equal to 8 .'}, 'sp', '20 / 1'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose age record is equal to 8 . among these rows , select the rows whose sp record fuzzily matches to 20 / 1 .', 'tostr': 'filter_eq { filter_eq { all_rows ; age ; 8 } ; sp ; 20 / 1 }'}], 'result': '1', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; age ; 8 } ; sp ; 20 / 1 } }', 'tointer': 'select the rows whose age record is equal to 8 . among these rows , select the rows whose sp record fuzzily matches to 20 / 1 . the number of such rows is 1 .'}, '1'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; age ; 8 } ; sp ; 20 / 1 } } ; 1 } = true', 'tointer': 'select the rows whose age record is equal to 8 . among these rows , select the rows whose sp record fuzzily matches to 20 / 1 . the number of such rows is 1 .'} | eq { count { filter_eq { filter_eq { all_rows ; age ; 8 } ; sp ; 20 / 1 } } ; 1 } = true | select the rows whose age record is equal to 8 . among these rows , select the rows whose sp record fuzzily matches to 20 / 1 . the number of such rows is 1 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_eq_0': 0, 'all_rows_5': 5, 'age_6': 6, '8_7': 7, 'sp_8': 8, '20 / 1_9': 9, '1_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_eq_0': 'filter_eq', 'all_rows_5': 'all_rows', 'age_6': 'age', '8_7': '8', 'sp_8': 'sp', '20 / 1_9': '20 / 1', '1_10': '1'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_eq_0': [1], 'all_rows_5': [0], 'age_6': [0], '8_7': [0], 'sp_8': [1], '20 / 1_9': [1], '1_10': [3]} | ['position', 'number', 'horse', 'jockey', 'age', 'handicap', 'sp', 'distance'] | [['1st', '21', 'bindaree', 'jim culloty', '8', '10 - 4', '20 / 1', 'won by 1 ¾ lengths'], ['2nd', '4', "what 's up boys", 'richard johnson', '8', '11 - 6', '10 / 1', '27 lengths'], ['3rd', '16', 'blowing wind', 'tony mccoy', '9', '10 - 6', '10 / 1', '9 lengths'], ['4th', '3', 'kingsmark', 'ruby walsh', '9', '11 - 9', '16 / 1', '17 lengths'], ['5th', '40', 'supreme charm', 'robert thornton', '10', '10 - 0', '28 / 1', '3 ½ lengths'], ['6th', '22', 'celibate', 'noel fehily', '11', '10 - 3', '66 / 1', '3 ½ lengths'], ['7th', '13', "you 're agoodun", 'johnny kavanagh', '10', '10 - 8', '50 / 1', '18 lengths'], ['8th', '14', 'royal predica', 'jimmy mccarthy', '8', '10 - 8', '80 / 1', '28 lengths'], ['9th', '12', 'streamstown', 'john mcnamara', '8', '10 - 8', '40 / 1', '13 lengths'], ['10th', '34', 'birkdale', 'jason maguire', '11', '10 - 2', '50 / 1', 'a distance']] |
grade ii * listed buildings in greater manchester | https://en.wikipedia.org/wiki/Grade_II%2A_listed_buildings_in_Greater_Manchester | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15906728-4.html.csv | ordinal | the second newest grade ii listed building in greater manchester is the church of st thomas . | {'row': '8', 'col': '4', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'completed', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; completed ; 2 }'}, 'name'], 'result': 'church of st thomas', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; completed ; 2 } ; name }'}, 'church of st thomas'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; completed ; 2 } ; name } ; church of st thomas } = true', 'tointer': 'select the row whose completed record of all rows is 2nd maximum . the name record of this row is church of st thomas .'} | eq { hop { nth_argmax { all_rows ; completed ; 2 } ; name } ; church of st thomas } = true | select the row whose completed record of all rows is 2nd maximum . the name record of this row is church of st thomas . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'completed_5': 5, '2_6': 6, 'name_7': 7, 'church of st thomas_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'completed_5': 'completed', '2_6': '2', 'name_7': 'name', 'church of st thomas_8': 'church of st thomas'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'completed_5': [0], '2_6': [0], 'name_7': [1], 'church of st thomas_8': [2]} | ['name', 'location', 'type', 'completed', 'list entry number'] | [['church of st chad', 'church lane , uppermill', 'church', '1746', '1162501'], ['grotton hall', 'platting road , lydgate', 'house', '1686', '1068157'], ['heights chapel , st thomas old church', 'broad lane , saddleworth', 'church', '1765', '1356677'], ['higher kinders', "kinder 's lane , saddleworth", 'house', '1642', '1068176'], ['shore mill', 'delph , saddleworth', 'carding mill', '1780s', '1067445'], ['church of st anne', "st anne 's avenue , royton", 'church', '1909', '1356418'], ['church of st mary with st peter', 'church street , oldham', 'parish church', '1830', '1292310'], ['church of st thomas', 'west street , lees', 'church', '1848', '1068071'], ['foxdenton hall', 'foxdenton lane , chadderton', 'house', '1730', '1356429'], ['independent methodist chapel', 'george street , oldham', 'methodist chapel', '1815', '1201672'], ['1 - 5 ηοllins road - also known as hathershaw hall', 'hollins road , oldham', 'house', '17th century', '1217873']] |
indra putra mahayuddin | https://en.wikipedia.org/wiki/Indra_Putra_Mahayuddin | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11847478-2.html.csv | count | in the games listed indra putra mahayuddin lost a total of five games . | {'scope': 'all', 'criterion': 'equal', 'value': 'lose', 'result': '5', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'lose'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to lose .', 'tostr': 'filter_eq { all_rows ; result ; lose }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; result ; lose } }', 'tointer': 'select the rows whose result record fuzzily matches to lose . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; result ; lose } } ; 5 } = true', 'tointer': 'select the rows whose result record fuzzily matches to lose . the number of such rows is 5 .'} | eq { count { filter_eq { all_rows ; result ; lose } } ; 5 } = true | select the rows whose result record fuzzily matches to lose . the number of such rows is 5 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'result_5': 5, 'lose_6': 6, '5_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'result_5': 'result', 'lose_6': 'lose', '5_7': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'result_5': [0], 'lose_6': [0], '5_7': [2]} | ['date', 'venue', 'score', 'result', 'competition'] | [['december 11 , 2002', 'petaling jaya , malaysia', '5 - 0', 'win', 'friendly'], ['december 18 , 2002', 'singapore , singapore', '0 - 4', 'win', '2002 tiger cup group stage'], ['december 20 , 2002', 'singapore , singapore', '3 - 1', 'win', '2002 tiger cup group stage'], ['december 29 , 2002', 'singapore , singapore', '2 - 1', 'lose', '2002 tiger cup third / fourth place'], ['october 22 , 2003', 'manama , bahrain', '3 - 1', 'lose', '2004 afc asian cup qualification'], ['august 23 , 2006', 'shah alam , malaysia', '1 - 2', 'lose', '2006 merdeka tournament group stage'], ['july 10 , 2007', 'kuala lumpur , malaysia', '1 - 5', 'lose', '2007 afc asian cup group stage'], ['july 22 , 2008', 'hyderabad , india', '1 - 1', 'draw', 'friendly'], ['october 15 , 2008', 'kelana jaya , malaysia', '4 - 0', 'win', '2008 merdeka tournament'], ['october 23 , 2008', 'kuala lumpur , malaysia', '4 - 0', 'win', '2008 merdeka tournament'], ['december 6 , 2008', 'phuket , thailand', '3 - 0', 'win', '2008 aff suzuki cup'], ['december 8 , 2008', 'phuket , thailand', '2 - 3', 'lose', '2008 aff suzuki cup']] |
athletics at the 2008 summer olympics - women 's 200 metres | https://en.wikipedia.org/wiki/Athletics_at_the_2008_Summer_Olympics_%E2%80%93_Women%27s_200_metres | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18569021-4.html.csv | superlative | veronica campbell-brown finished with the fastest time in the women 's 200 meters event in the 2008 summer olympics . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '3', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'time'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; time }'}, 'athlete'], 'result': 'veronica campbell - brown', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; time } ; athlete }'}, 'veronica campbell - brown'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; time } ; athlete } ; veronica campbell - brown } = true', 'tointer': 'select the row whose time record of all rows is minimum . the athlete record of this row is veronica campbell - brown .'} | eq { hop { argmin { all_rows ; time } ; athlete } ; veronica campbell - brown } = true | select the row whose time record of all rows is minimum . the athlete record of this row is veronica campbell - brown . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'time_5': 5, 'athlete_6': 6, 'veronica campbell - brown_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'time_5': 'time', 'athlete_6': 'athlete', 'veronica campbell - brown_7': 'veronica campbell - brown'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'time_5': [0], 'athlete_6': [1], 'veronica campbell - brown_7': [2]} | ['rank', 'lane', 'athlete', 'country', 'time', 'react'] | [['1', '5', 'veronica campbell - brown', 'jamaica', '22.19', '0.187'], ['2', '7', 'kerron stewart', 'jamaica', '22.29', '0.217'], ['3', '4', 'muna lee', 'united states', '22.29', '0.186'], ['4', '9', 'debbie ferguson - mckenzie', 'bahamas', '22.51', '0.165'], ['5', '6', 'yuliya chermoshanskaya', 'russia', '22.57', '0.204'], ['6', '3', 'nataliya pyhyda', 'ukraine', '22.95', '0.160'], ['7', '8', 'susanthika jayasinghe', 'sri lanka', '22.98', '0.245'], ['8', '2', 'roxana dã\xadaz', 'cuba', '23.12', '0.177']] |
2008 - 09 in scottish football | https://en.wikipedia.org/wiki/2008%E2%80%9309_in_Scottish_football | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17327458-1.html.csv | unique | jimmy calderwood was the only outgoing manager in the 2008 - 09 football season whose manner of departure was mutual consent . | {'scope': 'all', 'row': '13', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'mutual consent', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'manner of departure', 'mutual consent'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose manner of departure record fuzzily matches to mutual consent .', 'tostr': 'filter_eq { all_rows ; manner of departure ; mutual consent }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; manner of departure ; mutual consent } }', 'tointer': 'select the rows whose manner of departure record fuzzily matches to mutual consent . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'manner of departure', 'mutual consent'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose manner of departure record fuzzily matches to mutual consent .', 'tostr': 'filter_eq { all_rows ; manner of departure ; mutual consent }'}, 'outgoing manager'], 'result': 'jimmy calderwood', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; manner of departure ; mutual consent } ; outgoing manager }'}, 'jimmy calderwood'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; manner of departure ; mutual consent } ; outgoing manager } ; jimmy calderwood }', 'tointer': 'the outgoing manager record of this unqiue row is jimmy calderwood .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; manner of departure ; mutual consent } } ; eq { hop { filter_eq { all_rows ; manner of departure ; mutual consent } ; outgoing manager } ; jimmy calderwood } } = true', 'tointer': 'select the rows whose manner of departure record fuzzily matches to mutual consent . there is only one such row in the table . the outgoing manager record of this unqiue row is jimmy calderwood .'} | and { only { filter_eq { all_rows ; manner of departure ; mutual consent } } ; eq { hop { filter_eq { all_rows ; manner of departure ; mutual consent } ; outgoing manager } ; jimmy calderwood } } = true | select the rows whose manner of departure record fuzzily matches to mutual consent . there is only one such row in the table . the outgoing manager record of this unqiue row is jimmy calderwood . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'manner of departure_7': 7, 'mutual consent_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'outgoing manager_9': 9, 'jimmy calderwood_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'manner of departure_7': 'manner of departure', 'mutual consent_8': 'mutual consent', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'outgoing manager_9': 'outgoing manager', 'jimmy calderwood_10': 'jimmy calderwood'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'manner of departure_7': [0], 'mutual consent_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'outgoing manager_9': [2], 'jimmy calderwood_10': [3]} | ['team', 'outgoing manager', 'manner of departure', 'date of vacancy', 'replaced by', 'date of appointment'] | [['albion rovers', 'john mccormack', 'resigned', '28 june', 'paul martin', '9 july'], ['heart of midlothian', 'stephen frail', 'sacked', '9 july', 'csaba lászló', '11 july'], ['dundee', 'alex rae', 'sacked', '20 october', 'jocky scott', '30 october'], ['montrose', 'jim weir', 'sacked', '19 october', 'steven tweed', '15 january'], ['berwick rangers', 'alan mcgonigal', 'resigned', '13 november', 'jimmy crease', '26 december'], ['livingston', 'roberto landi', 'sacked', '1 december', 'paul hegarty', '5 december'], ['brechin city', "michael o'neill", 'resigned', '15 december', 'jim duffy', '9 january'], ['elgin city', 'robbie williamson', 'resigned', '20 december', 'ross jack', '23 january'], ['inverness ct', 'craig brewster', 'sacked', '19 january', 'terry butcher', '27 january'], ['stranraer', 'derek ferguson', 'resigned', '24 january', 'keith knox', '17 february'], ['east fife', 'dave baikie', 'resigned', '14 april', 'stevie crawford', '14 april'], ['livingston', 'paul hegarty', 'suspended', '25 april', 'john murphy', '30 june'], ['aberdeen', 'jimmy calderwood', 'mutual consent', '24 may', 'mark mcghee', '12 june'], ['celtic', 'gordon strachan', 'resigned', '25 may', 'tony mowbray', '16 june'], ['hibernian', 'mixu paatelainen', 'resigned', '29 may', 'john hughes', '8 june'], ['falkirk', 'john hughes', 'resigned', '8 june', 'eddie may', '23 june']] |
1975 england rugby union tour of australia | https://en.wikipedia.org/wiki/1975_England_rugby_union_tour_of_Australia | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17004899-1.html.csv | comparative | queensland country scored fewer points than new south wales against england in the 1975 england rugby union tour of australia . | {'row_1': '6', 'row_2': '3', 'col': '2', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opposing team', 'queensland'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opposing team record fuzzily matches to queensland .', 'tostr': 'filter_eq { all_rows ; opposing team ; queensland }'}, 'against'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opposing team ; queensland } ; against }', 'tointer': 'select the rows whose opposing team record fuzzily matches to queensland . take the against record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opposing team', 'new south wales'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opposing team record fuzzily matches to new south wales .', 'tostr': 'filter_eq { all_rows ; opposing team ; new south wales }'}, 'against'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opposing team ; new south wales } ; against }', 'tointer': 'select the rows whose opposing team record fuzzily matches to new south wales . take the against record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; opposing team ; queensland } ; against } ; hop { filter_eq { all_rows ; opposing team ; new south wales } ; against } } = true', 'tointer': 'select the rows whose opposing team record fuzzily matches to queensland . take the against record of this row . select the rows whose opposing team record fuzzily matches to new south wales . take the against record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; opposing team ; queensland } ; against } ; hop { filter_eq { all_rows ; opposing team ; new south wales } ; against } } = true | select the rows whose opposing team record fuzzily matches to queensland . take the against record of this row . select the rows whose opposing team record fuzzily matches to new south wales . take the against record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'opposing team_7': 7, 'queensland_8': 8, 'against_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opposing team_11': 11, 'new south wales_12': 12, 'against_13': 13} | {'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'opposing team_7': 'opposing team', 'queensland_8': 'queensland', 'against_9': 'against', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opposing team_11': 'opposing team', 'new south wales_12': 'new south wales', 'against_13': 'against'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opposing team_7': [0], 'queensland_8': [0], 'against_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opposing team_11': [1], 'new south wales_12': [1], 'against_13': [3]} | ['opposing team', 'against', 'date', 'venue', 'status'] | [['western australia', '12', '10 / 05 / 1975', 'perry lakes stadium , perth', 'tour match'], ['sydney', '14', '13 / 05 / 1975', 'sydney cricket ground , sydney', 'tour match'], ['new south wales', '24', '17 / 05 / 1975', 'sydney sports ground , sydney', 'tour match'], ['new south wales country xv', '14', '20 / 05 / 1975', 'goulburn', 'tour match'], ['australia', '16', '24 / 05 / 1975', 'sydney cricket ground , sydney', 'first test'], ['queensland', '3', '27 / 05 / 1975', 'ballymore , brisbane', 'tour match'], ['australia', '30', '31 / 05 / 1975', 'ballymore , brisbane', 'second test'], ['queensland country', '6', '03 / 06 / 1975', 'townsville sports reserve , townsville', 'tour match']] |
indiana high school athletics conferences : ohio river valley - western indiana | https://en.wikipedia.org/wiki/Indiana_High_School_Athletics_Conferences%3A_Ohio_River_Valley_%E2%80%93_Western_Indiana | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18974097-16.html.csv | comparative | owen valley had more students enrolled than sullivan in the ohio river valley - western indiana conference . | {'row_1': '4', 'row_2': '6', 'col': '4', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'school', 'owen valley'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose school record fuzzily matches to owen valley .', 'tostr': 'filter_eq { all_rows ; school ; owen valley }'}, 'enrollment'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; school ; owen valley } ; enrollment }', 'tointer': 'select the rows whose school record fuzzily matches to owen valley . take the enrollment record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'school', 'sullivan'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose school record fuzzily matches to sullivan .', 'tostr': 'filter_eq { all_rows ; school ; sullivan }'}, 'enrollment'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; school ; sullivan } ; enrollment }', 'tointer': 'select the rows whose school record fuzzily matches to sullivan . take the enrollment record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; school ; owen valley } ; enrollment } ; hop { filter_eq { all_rows ; school ; sullivan } ; enrollment } } = true', 'tointer': 'select the rows whose school record fuzzily matches to owen valley . take the enrollment record of this row . select the rows whose school record fuzzily matches to sullivan . take the enrollment record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; school ; owen valley } ; enrollment } ; hop { filter_eq { all_rows ; school ; sullivan } ; enrollment } } = true | select the rows whose school record fuzzily matches to owen valley . take the enrollment record of this row . select the rows whose school record fuzzily matches to sullivan . take the enrollment record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'school_7': 7, 'owen valley_8': 8, 'enrollment_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'school_11': 11, 'sullivan_12': 12, 'enrollment_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'school_7': 'school', 'owen valley_8': 'owen valley', 'enrollment_9': 'enrollment', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'school_11': 'school', 'sullivan_12': 'sullivan', 'enrollment_13': 'enrollment'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'school_7': [0], 'owen valley_8': [0], 'enrollment_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'school_11': [1], 'sullivan_12': [1], 'enrollment_13': [3]} | ['school', 'location', 'mascot', 'enrollment', 'ihsaa class', 'ihsaa football class', 'county'] | [['brown county', 'nashville', 'eagles', '755', 'aaa', 'aaa', '7 brown'], ['edgewood', 'ellettsville', 'mustangs', '833', 'aaa', 'aaa', '53 monroe'], ['northview', 'brazil', 'knights', '1142', 'aaaa', 'aaaa', '11 clay'], ['owen valley', 'spencer', 'patriots', '908', 'aaa', 'aaaa', '60 owen'], ['south vermillion', 'clinton', 'wildcats', '583', 'aaa', 'aa', '83 vermillion'], ['sullivan', 'sullivan', 'golden arrows', '543', 'aaa', 'aa', '77 sullivan'], ['west vigo', 'west terre haute', 'vikings', '640', 'aaa', 'aaa', '84 vigo']] |
1949 vfl season | https://en.wikipedia.org/wiki/1949_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10809351-1.html.csv | count | in the 1949 vfl season , when the away team had under 10 , there were 2 games where the crowd was over 20000 . | {'scope': 'subset', 'criterion': 'greater_than', 'value': '20000', 'result': '2', 'col': '6', 'subset': {'col': '4', 'criterion': 'less_than', 'value': '10'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'away team score', '10'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; away team score ; 10 }', 'tointer': 'select the rows whose away team score record is less than 10 .'}, 'crowd', '20000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose away team score record is less than 10 . among these rows , select the rows whose crowd record is greater than 20000 .', 'tostr': 'filter_greater { filter_less { all_rows ; away team score ; 10 } ; crowd ; 20000 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_greater { filter_less { all_rows ; away team score ; 10 } ; crowd ; 20000 } }', 'tointer': 'select the rows whose away team score record is less than 10 . among these rows , select the rows whose crowd record is greater than 20000 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_greater { filter_less { all_rows ; away team score ; 10 } ; crowd ; 20000 } } ; 2 } = true', 'tointer': 'select the rows whose away team score record is less than 10 . among these rows , select the rows whose crowd record is greater than 20000 . the number of such rows is 2 .'} | eq { count { filter_greater { filter_less { all_rows ; away team score ; 10 } ; crowd ; 20000 } } ; 2 } = true | select the rows whose away team score record is less than 10 . among these rows , select the rows whose crowd record is greater than 20000 . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_greater_1': 1, 'filter_less_0': 0, 'all_rows_5': 5, 'away team score_6': 6, '10_7': 7, 'crowd_8': 8, '20000_9': 9, '2_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_greater_1': 'filter_greater', 'filter_less_0': 'filter_less', 'all_rows_5': 'all_rows', 'away team score_6': 'away team score', '10_7': '10', 'crowd_8': 'crowd', '20000_9': '20000', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_greater_1': [2], 'filter_less_0': [1], 'all_rows_5': [0], 'away team score_6': [0], '10_7': [0], 'crowd_8': [1], '20000_9': [1], '2_10': [3]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['geelong', '21.19 ( 145 )', 'melbourne', '12.11 ( 83 )', 'kardinia park', '25000', '16 april 1949'], ['essendon', '18.12 ( 120 )', 'hawthorn', '9.3 ( 57 )', 'windy hill', '13500', '16 april 1949'], ['collingwood', '19.13 ( 127 )', 'north melbourne', '10.17 ( 77 )', 'victoria park', '21500', '16 april 1949'], ['st kilda', '11.10 ( 76 )', 'fitzroy', '14.14 ( 98 )', 'junction oval', '18000', '16 april 1949'], ['carlton', '16.16 ( 112 )', 'south melbourne', '6.12 ( 48 )', 'princes park', '29000', '18 april 1949'], ['richmond', '19.13 ( 127 )', 'footscray', '8.10 ( 58 )', 'punt road oval', '30000', '18 april 1949']] |
1991 pga championship | https://en.wikipedia.org/wiki/1991_PGA_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18130923-1.html.csv | superlative | in the 1991 pga championship , david graham scored the largest total among players from australia . | {'scope': 'subset', 'col_superlative': '4', 'row_superlative': '7', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1,2', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'australia'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'australia'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; country ; australia }', 'tointer': 'select the rows whose country record fuzzily matches to australia .'}, 'total'], 'result': None, 'ind': 1, 'tostr': 'argmax { filter_eq { all_rows ; country ; australia } ; total }'}, 'player'], 'result': 'david graham', 'ind': 2, 'tostr': 'hop { argmax { filter_eq { all_rows ; country ; australia } ; total } ; player }'}, 'david graham'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { argmax { filter_eq { all_rows ; country ; australia } ; total } ; player } ; david graham } = true', 'tointer': 'select the rows whose country record fuzzily matches to australia . select the row whose total record of these rows is maximum . the player record of this row is david graham .'} | eq { hop { argmax { filter_eq { all_rows ; country ; australia } ; total } ; player } ; david graham } = true | select the rows whose country record fuzzily matches to australia . select the row whose total record of these rows is maximum . the player record of this row is david graham . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'argmax_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'country_6': 6, 'australia_7': 7, 'total_8': 8, 'player_9': 9, 'david graham_10': 10} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'argmax_1': 'argmax', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'country_6': 'country', 'australia_7': 'australia', 'total_8': 'total', 'player_9': 'player', 'david graham_10': 'david graham'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'argmax_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'country_6': [0], 'australia_7': [0], 'total_8': [1], 'player_9': [2], 'david graham_10': [3]} | ['player', 'country', 'year ( s ) won', 'total', 'to par', 'finish'] | [['raymond floyd', 'united states', '1969 , 1982', '284', '- 4', 't7'], ['hal sutton', 'united states', '1983', '284', '- 4', 't7'], ['payne stewart', 'united states', '1989', '285', '- 3', 't13'], ['jack nicklaus', 'united states', '1963 , 1971 , 1973 1975 , 1980', '287', '- 1', 't23'], ['wayne grady', 'australia', '1990', '290', '+ 2', 't43'], ['lanny wadkins', 'united states', '1977', '290', '+ 2', 't43'], ['david graham', 'australia', '1979', '292', '+ 4', 't52'], ['jeff sluman', 'united states', '1988', '294', '+ 6', 't61'], ['bob tway', 'united states', '1986', '296', '+ 8', 't66']] |
1996 u.s. open ( golf ) | https://en.wikipedia.org/wiki/1996_U.S._Open_%28golf%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17162199-6.html.csv | majority | in the 1996 u.s. open , most players scored over par . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '0', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'to par', '0'], 'result': True, 'ind': 0, 'tointer': 'for the to par records of all rows , most of them are greater than 0 .', 'tostr': 'most_greater { all_rows ; to par ; 0 } = true'} | most_greater { all_rows ; to par ; 0 } = true | for the to par records of all rows , most of them are greater than 0 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'to par_3': 3, '0_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'to par_3': 'to par', '0_4': '0'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'to par_3': [0], '0_4': [0]} | ['place', 'player', 'country', 'score', 'to par', 'money'] | [['1', 'steve jones', 'united states', '74 + 66 + 69 + 69 = 278', '- 2', '425000'], ['t2', 'tom lehman', 'united states', '71 + 72 + 65 + 71 = 279', '- 1', '204801'], ['t2', 'davis love iii', 'united states', '71 + 69 + 70 + 69 = 279', '- 1', '204801'], ['4', 'john morse', 'united states', '68 + 74 + 68 + 70 = 280', 'e', '111235'], ['t5', 'ernie els', 'south africa', '72 + 67 + 72 + 70 = 281', '+ 1', '84965'], ['t5', 'jim furyk', 'united states', '72 + 69 + 70 + 70 = 281', '+ 1', '84965'], ['t7', 'ken green', 'united states', '73 + 67 + 72 + 70 = 282', '+ 2', '66295'], ['t7', 'scott hoch', 'united states', '73 + 71 + 71 + 67 = 282', '+ 2', '66295'], ['t7', 'vijay singh', 'fiji', '71 + 72 + 70 + 69 = 282', '+ 2', '66295'], ['t10', 'lee janzen', 'united states', '68 + 75 + 71 + 69 = 283', '+ 3', '52591'], ['t10', 'colin montgomerie', 'scotland', '70 + 72 + 69 + 72 = 283', '+ 3', '52591'], ['t10', 'greg norman', 'australia', '73 + 66 + 74 + 70 = 283', '+ 3', '52591']] |
bmw m1 procar championship | https://en.wikipedia.org/wiki/BMW_M1_Procar_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18261246-1.html.csv | comparative | elio de angelis managed to win a race before niki lauda . | {'row_1': '1', 'row_2': '2', 'col': '2', 'col_other': '5', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winning driver', 'elio de angelis'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winning driver record fuzzily matches to elio de angelis .', 'tostr': 'filter_eq { all_rows ; winning driver ; elio de angelis }'}, 'date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; winning driver ; elio de angelis } ; date }', 'tointer': 'select the rows whose winning driver record fuzzily matches to elio de angelis . take the date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winning driver', 'niki lauda'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose winning driver record fuzzily matches to niki lauda .', 'tostr': 'filter_eq { all_rows ; winning driver ; niki lauda }'}, 'date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; winning driver ; niki lauda } ; date }', 'tointer': 'select the rows whose winning driver record fuzzily matches to niki lauda . take the date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; winning driver ; elio de angelis } ; date } ; hop { filter_eq { all_rows ; winning driver ; niki lauda } ; date } } = true', 'tointer': 'select the rows whose winning driver record fuzzily matches to elio de angelis . take the date record of this row . select the rows whose winning driver record fuzzily matches to niki lauda . take the date record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; winning driver ; elio de angelis } ; date } ; hop { filter_eq { all_rows ; winning driver ; niki lauda } ; date } } = true | select the rows whose winning driver record fuzzily matches to elio de angelis . take the date record of this row . select the rows whose winning driver record fuzzily matches to niki lauda . take the date record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'winning driver_7': 7, 'elio de angelis_8': 8, 'date_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'winning driver_11': 11, 'niki lauda_12': 12, 'date_13': 13} | {'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'winning driver_7': 'winning driver', 'elio de angelis_8': 'elio de angelis', 'date_9': 'date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'winning driver_11': 'winning driver', 'niki lauda_12': 'niki lauda', 'date_13': 'date'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'winning driver_7': [0], 'elio de angelis_8': [0], 'date_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'winning driver_11': [1], 'niki lauda_12': [1], 'date_13': [3]} | ['round', 'date', 'event', 'circuit', 'winning driver', 'winning team'] | [['1', 'may 12', 'belgian grand prix', 'circuit zolder', 'elio de angelis', 'squadra osella corse'], ['2', 'may 26', 'monaco grand prix', 'circuit de monaco', 'niki lauda', 'project four'], ['-', 'june 3', 'gunnar nilsson trophy', 'donington park', 'nelson piquet', 'bmw motorsport'], ['3', 'june 30', 'french grand prix', 'dijon - prenois', 'nelson piquet', 'bmw motorsport'], ['4', 'july 13', 'british grand prix', 'silverstone circuit', 'niki lauda', 'project four'], ['5', 'july 28', 'german grand prix', 'hockenheimring', 'niki lauda', 'project four'], ['6', 'august 11', 'austrian grand prix', 'österreichring', 'jacques laffite', 'bmw motorsport'], ['7', 'august 25', 'dutch grand prix', 'circuit zandvoort', 'hans - joachim stuck', 'cassani racing'], ['8', 'september 8', 'italian grand prix', 'autodromo nazionale monza', 'hans - joachim stuck', 'cassani racing']] |
wru division one west | https://en.wikipedia.org/wiki/WRU_Division_One_West | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12792876-2.html.csv | ordinal | in wru division one west , the club maesteg rfc had the 2nd most points against . | {'row': '12', 'col': '6', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'points against', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; points against ; 2 }'}, 'club'], 'result': 'maesteg rfc', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; points against ; 2 } ; club }'}, 'maesteg rfc'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; points against ; 2 } ; club } ; maesteg rfc } = true', 'tointer': 'select the row whose points against record of all rows is 2nd maximum . the club record of this row is maesteg rfc .'} | eq { hop { nth_argmax { all_rows ; points against ; 2 } ; club } ; maesteg rfc } = true | select the row whose points against record of all rows is 2nd maximum . the club record of this row is maesteg rfc . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'points against_5': 5, '2_6': 6, 'club_7': 7, 'maesteg rfc_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'points against_5': 'points against', '2_6': '2', 'club_7': 'club', 'maesteg rfc_8': 'maesteg rfc'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'points against_5': [0], '2_6': [0], 'club_7': [1], 'maesteg rfc_8': [2]} | ['club', 'played', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus', 'losing bonus', 'points'] | [['club', 'played', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus', 'losing bonus', 'points'], ['bridgend ravens', '22', '1', '1', '848', '337', '108', '30', '13', '1', '96'], ['narberth rfc', '22', '1', '8', '726', '443', '92', '53', '12', '5', '71'], ['bridgend athletic rfc', '22', '3', '5', '564', '486', '61', '55', '5', '1', '68'], ['bonymaen rfc', '22', '2', '6', '478', '464', '55', '55', '5', '3', '68'], ['corus ( port talbot ) rfc', '22', '1', '8', '576', '544', '73', '58', '10', '4', '68'], ['uwic rfc', '22', '1', '9', '624', '559', '80', '66', '10', '4', '64'], ['whitland rfc', '22', '2', '9', '550', '460', '69', '49', '6', '3', '57'], ['carmarthen athletic rfc', '22', '3', '10', '509', '554', '64', '69', '6', '2', '50'], ['llangennech rfc', '22', '0', '14', '402', '577', '46', '69', '4', '3', '39'], ['waunarlwydd rfc', '22', '0', '16', '505', '602', '48', '75', '3', '10', '37'], ['maesteg rfc', '22', '0', '19', '427', '714', '43', '91', '2', '5', '19'], ['felinfoel rfc', '22', '2', '19', '334', '803', '43', '112', '3', '5', '16']] |
naoki tsukahara | https://en.wikipedia.org/wiki/Naoki_Tsukahara | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11401861-1.html.csv | unique | the only time naoki tsukahara finished in 7th position was at the 58th national sports festival of japan . | {'scope': 'all', 'row': '1', 'col': '4', 'col_other': '2', 'criterion': 'equal', 'value': '7th', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', '7th'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to 7th .', 'tostr': 'filter_eq { all_rows ; position ; 7th }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; position ; 7th } }', 'tointer': 'select the rows whose position record fuzzily matches to 7th . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', '7th'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to 7th .', 'tostr': 'filter_eq { all_rows ; position ; 7th }'}, 'competition'], 'result': '58th national sports festival of japan', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; position ; 7th } ; competition }'}, '58th national sports festival of japan'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; position ; 7th } ; competition } ; 58th national sports festival of japan }', 'tointer': 'the competition record of this unqiue row is 58th national sports festival of japan .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; position ; 7th } } ; eq { hop { filter_eq { all_rows ; position ; 7th } ; competition } ; 58th national sports festival of japan } } = true', 'tointer': 'select the rows whose position record fuzzily matches to 7th . there is only one such row in the table . the competition record of this unqiue row is 58th national sports festival of japan .'} | and { only { filter_eq { all_rows ; position ; 7th } } ; eq { hop { filter_eq { all_rows ; position ; 7th } ; competition } ; 58th national sports festival of japan } } = true | select the rows whose position record fuzzily matches to 7th . there is only one such row in the table . the competition record of this unqiue row is 58th national sports festival of japan . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'position_7': 7, '7th_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'competition_9': 9, '58th national sports festival of japan_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'position_7': 'position', '7th_8': '7th', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'competition_9': 'competition', '58th national sports festival of japan_10': '58th national sports festival of japan'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'position_7': [0], '7th_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'competition_9': [2], '58th national sports festival of japan_10': [3]} | ['year', 'competition', 'venue', 'position', 'notes'] | [['2003', '58th national sports festival of japan', 'shizuoka , japan', '7th', '100 m'], ['2004', 'japan student athletics championships', 'unknown , japan', '6th', '200 m'], ['2004', 'world junior championships', 'grosseto , italy', '3rd', '4x100 m relay'], ['2006', 'kanto students athletics championships', 'kantō , japan', '2nd', '100 m'], ['2006', 'kanto students athletics championships', 'kantō , japan', '2nd', '200 m'], ['2006', 'japan association of athletics championships', 'tokyo , japan', '1st', '100 m'], ['2006', 'japan association of athletics championships', 'tokyo , japan', '3rd', '200 m'], ['2006', 'world cup', 'athens , greece', '3rd', '4x100 m relay'], ['2006', 'asian games', 'doha , qatar', '2nd', '100 m'], ['2006', 'asian games', 'doha , qatar', '2nd', '4x100 m relay'], ['2008', 'olympic games', 'beijing , china', '3rd', '4x100 m relay']] |
united states house of representatives elections , 1812 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1812 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2668367-7.html.csv | majority | the large majority of the districts had either democratic-republican candidates . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'democratic - republican', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'party', 'democratic - republican'], 'result': True, 'ind': 0, 'tointer': 'for the party records of all rows , most of them fuzzily match to democratic - republican .', 'tostr': 'most_eq { all_rows ; party ; democratic - republican } = true'} | most_eq { all_rows ; party ; democratic - republican } = true | for the party records of all rows , most of them fuzzily match to democratic - republican . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'party_3': 3, 'democratic - republican_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'party_3': 'party', 'democratic - republican_4': 'democratic - republican'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'party_3': [0], 'democratic - republican_4': [0]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['kentucky 1', 'none ( district created )', 'none ( district created )', 'none ( district created )', 'new seat democratic - republican gain', 'james clark ( dr ) 100 %'], ['kentucky 2', 'henry clay redistricted from the 5th district', 'democratic - republican', '1810', 're - elected', 'henry clay ( dr ) 100 %'], ['kentucky 4', 'joseph desha redistricted from the 6th district', 'democratic - republican', '1806', 're - elected', 'joseph desha ( dr ) 100 %'], ['kentucky 6', 'none ( district created )', 'none ( district created )', 'none ( district created )', 'new seat democratic - republican gain', 'solomon p sharp ( dr ) 69.9 % anthony butler 30.1 %'], ['kentucky 7', 'samuel mckee redistricted from the 2nd district', 'democratic - republican', '1808', 're - elected', 'samuel mckee ( dr ) 100 %'], ['kentucky 8', 'stephen ormsby redistricted from the 3rd district', 'democratic - republican', '1810', 'lost re - election democratic - republican hold', 'john simpson ( dr ) stephen ormsby ( dr )'], ['kentucky 9', 'none ( district created )', 'none ( district created )', 'none ( district created )', 'new seat democratic - republican gain', 'thomas montgomery ( dr ) henry james micah taul ( dr )']] |
weightlifting at the 1999 pan american games | https://en.wikipedia.org/wiki/Weightlifting_at_the_1999_Pan_American_Games | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11279593-4.html.csv | count | two athletes lifted a total weight of 300.0 kg . | {'scope': 'all', 'criterion': 'equal', 'value': '300.0', 'result': '2', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'total ( kg )', '300.0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose total ( kg ) record is equal to 300.0 .', 'tostr': 'filter_eq { all_rows ; total ( kg ) ; 300.0 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; total ( kg ) ; 300.0 } }', 'tointer': 'select the rows whose total ( kg ) record is equal to 300.0 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; total ( kg ) ; 300.0 } } ; 2 } = true', 'tointer': 'select the rows whose total ( kg ) record is equal to 300.0 . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; total ( kg ) ; 300.0 } } ; 2 } = true | select the rows whose total ( kg ) record is equal to 300.0 . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'total (kg)_5': 5, '300.0_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'total (kg)_5': 'total ( kg )', '300.0_6': '300.0', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'total (kg)_5': [0], '300.0_6': [0], '2_7': [2]} | ['name', 'bodyweight', 'snatch', 'clean & jerk', 'total ( kg )'] | [['idalberto aranda ( cub )', '76.55', '150.0', '205.5 wr', '355.0'], ['walter llerena ( ecu )', '76.78', '150.0', '182.5', '332.5'], ['oscar chaplin iii ( usa )', '76.95', '150.0', '182.5', '332.5'], ['carlos sauri ( pur )', '76.91', '140.0', '165.0', '305.0'], ['marcelo gandolfo ( arg )', '76.25', '130.0', '170.0', '300.0'], ['guy hamilton ( can )', '76.86', '132.5', '167.5', '300.0'], ['edward silva ( uru )', '76.22', '122.5', '145.0', '267.5'], ['luis urriche ( chi )', '76.18', '127.5', '152.5', '']] |
1962 vfl season | https://en.wikipedia.org/wiki/1962_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10776868-6.html.csv | ordinal | the game at victoria park had the second largest crowd . | {'row': '6', 'col': '6', 'order': '2', 'col_other': '5', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'crowd', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; crowd ; 2 }'}, 'venue'], 'result': 'victoria park', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; crowd ; 2 } ; venue }'}, 'victoria park'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; victoria park } = true', 'tointer': 'select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is victoria park .'} | eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; victoria park } = true | select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is victoria park . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'crowd_5': 5, '2_6': 6, 'venue_7': 7, 'victoria park_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'crowd_5': 'crowd', '2_6': '2', 'venue_7': 'venue', 'victoria park_8': 'victoria park'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'crowd_5': [0], '2_6': [0], 'venue_7': [1], 'victoria park_8': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['melbourne', '8.15 ( 63 )', 'south melbourne', '1.11 ( 17 )', 'mcg', '25737', '26 may 1962'], ['fitzroy', '11.13 ( 79 )', 'hawthorn', '8.8 ( 56 )', 'brunswick street oval', '14781', '26 may 1962'], ['essendon', '14.9 ( 93 )', 'footscray', '13.9 ( 87 )', 'windy hill', '37000', '26 may 1962'], ['st kilda', '13.10 ( 88 )', 'richmond', '12.8 ( 80 )', 'junction oval', '33600', '26 may 1962'], ['north melbourne', '6.14 ( 50 )', 'geelong', '19.15 ( 129 )', 'arden street oval', '14140', '26 may 1962'], ['collingwood', '12.8 ( 80 )', 'carlton', '10.4 ( 64 )', 'victoria park', '34528', '26 may 1962']] |
eurovision dance contest 2007 | https://en.wikipedia.org/wiki/Eurovision_Dance_Contest_2007 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10530468-1.html.csv | aggregation | the average number of points received by the teams in the eurodance dance contest 2007 was 58 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '58', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'points'], 'result': '58', 'ind': 0, 'tostr': 'avg { all_rows ; points }'}, '58'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; points } ; 58 } = true', 'tointer': 'the average of the points record of all rows is 58 .'} | round_eq { avg { all_rows ; points } ; 58 } = true | the average of the points record of all rows is 58 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'points_4': 4, '58_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'points_4': 'points', '58_5': '58'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'points_4': [0], '58_5': [1]} | ['draw', 'dancers', 'dance styles', 'place', 'points'] | [['01', 'denise biellmann & sven ninnemann', 'paso doble and swing', '16', '0'], ['02', 'mariya sittel & vladislav borodinov', 'rumba and paso doble', '7', '72'], ['03', 'alexandra matteman & redmond valk', 'cha - cha - cha and rumba', '12', '34'], ['04', 'camilla dallerup & brendan cole', 'rumba and freestyle', '15', '18'], ['05', 'kelly & andy kainz', 'jive and paso doble', '5', '74'], ['06', 'wolke hegenbarth & oliver seefeldt', 'samba dance and freestyle', '8', '59'], ['07', 'ourania kolliou & spiros pavlidis', 'jive and sirtaki', '13', '31'], ['08', 'gabrielė valiukaitė & gintaras svistunavičius', 'paso doble and traditional lithuanian folk dance', '11', '35'], ['09', 'amagoya benlloch & abraham martinez', 'cha - cha - cha and paso doble', '10', '38'], ['10', 'nicola byrne & mick donegan', 'jive and fandango', '3', '95'], ['11', 'katarzyna cichopek & marcin hakiel', 'cha - cha - cha and showdance', '4', '84'], ['12', 'mette skou elkjær & david jørgensen', 'rumba and showdance', '9', '38'], ['13', 'sónia araújo & ricardo silva', 'jive and tango', '5', '74'], ['14', 'yulia okropiridze & illya sydorenko', 'quickstep and showdance', '2', '121'], ['15', 'cecilia ehrling & martin lidberg', 'paso doble and disco fusion', '14', '23'], ['16', 'katja koukkula & jussi väänänen', 'rumba and paso doble', '1', '132']] |
livonia cup | https://en.wikipedia.org/wiki/Livonia_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14157023-1.html.csv | count | there are 5 recorded seasons of the livonia cup . | {'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '5', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'season'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose season record is arbitrary .', 'tostr': 'filter_all { all_rows ; season }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; season } }', 'tointer': 'select the rows whose season record is arbitrary . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; season } } ; 5 } = true', 'tointer': 'select the rows whose season record is arbitrary . the number of such rows is 5 .'} | eq { count { filter_all { all_rows ; season } } ; 5 } = true | select the rows whose season record is arbitrary . the number of such rows is 5 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'season_5': 5, '5_6': 6} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'season_5': 'season', '5_6': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'season_5': [0], '5_6': [2]} | ['season', 'winner', 'score', 'runner - up', 'venue'] | [['2011', 'fc flora tallinn', '2 - 0', 'skonto fc', 'skonto hall , riga'], ['2008', 'fk ventspils', '2 - 2 aet , 4 - 3 pen', 'fc levadia tallinn', 'skonto hall , riga'], ['2005', 'skonto fc', '4 - 3', 'fc levadia tallinn', 'skonto hall , riga'], ['2004', 'skonto fc', '3 - 3 aet , 4 - 3 pen', 'fc flora tallinn', 'skonto hall , riga'], ['2003', 'skonto fc', '2 - 2 aet , 12 - 11 pen', 'fc flora tallinn', 'skonto hall , riga']] |
branimir suba \ xc5 \ xa1i \ xc4 \ x87 | https://en.wikipedia.org/wiki/Branimir_Suba%C5%A1i%C4%87 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11978803-1.html.csv | count | branimir subašić won four of the matches that they participated in . | {'scope': 'all', 'criterion': 'equal', 'value': 'win', 'result': '4', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'win'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to win .', 'tostr': 'filter_eq { all_rows ; result ; win }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; result ; win } }', 'tointer': 'select the rows whose result record fuzzily matches to win . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; result ; win } } ; 4 } = true', 'tointer': 'select the rows whose result record fuzzily matches to win . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; result ; win } } ; 4 } = true | select the rows whose result record fuzzily matches to win . the number of such rows is 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'result_5': 5, 'win_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'result_5': 'result', 'win_6': 'win', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'result_5': [0], 'win_6': [0], '4_7': [2]} | ['date', 'venue', 'score', 'result', 'competition'] | [['march 7 , 2007', 'shymkent , kazakhstan', '0 - 1', 'win', 'friendly'], ['june 2 , 2007', 'baku , azerbaijan', '1 - 3', 'lost', 'uefa euro 2008 qualifying'], ['august 22 , 2007', 'dushanbe , tajikistan', '2 - 3', 'win', 'friendly'], ['september 12 , 2007', 'baku , azerbaijan', '1 - 1', 'draw', 'friendly'], ['june 4 , 2008', 'andorra la vella , andorra', '1 - 2', 'win', 'friendly'], ['november 19 , 2008', 'baku , azerbaijan', '1 - 1', 'draw', 'friendly'], ['august 15 , 2012', 'baku , azerbaijan', '3 - 0', 'win', 'friendly']] |
new zealand national football team | https://en.wikipedia.org/wiki/New_Zealand_national_football_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1023035-3.html.csv | superlative | vaughan coveny had the most caps out of all the players on the new zealand national football team . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'caps'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; caps }'}, 'name'], 'result': 'vaughan coveny', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; caps } ; name }'}, 'vaughan coveny'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; caps } ; name } ; vaughan coveny } = true', 'tointer': 'select the row whose caps record of all rows is maximum . the name record of this row is vaughan coveny .'} | eq { hop { argmax { all_rows ; caps } ; name } ; vaughan coveny } = true | select the row whose caps record of all rows is maximum . the name record of this row is vaughan coveny . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'caps_5': 5, 'name_6': 6, 'vaughan coveny_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'caps_5': 'caps', 'name_6': 'name', 'vaughan coveny_7': 'vaughan coveny'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'caps_5': [0], 'name_6': [1], 'vaughan coveny_7': [2]} | ['name', 'career', 'goals', 'caps', 'first cap', 'most recent cap'] | [['vaughan coveny', '1992 - 2006', '28', '64', '7 june 1992', '4 june 2006'], ['shane smeltz', '2003 -', '23', '49', 'united states 9 june 2003', 'new caledonia 21 march 2013'], ['steve sumner', '1976 - 1988', '22', '58', 'burma 13 september 1976', '23 june 1988'], ['brian turner', '1967 - 1982', '21', '59', 'australia 5 november 1967', '23 june 1982'], ['jock newall', '1951 - 1952', '17', '10', 'new caledonia 19 september 1951', 'new caledonia 28 september 1952'], ['keith nelson', '1977 - 1983', '16', '20', 'new caledonia 5 march 1977', '7 june 1983'], ['chris killen', '2000 -', '16', '48', 'tahiti 19 june 2000', '5 september 2013'], ['grant turner', '1980 - 1988', '15', '42', '20 august 1980', '27 march 1988'], ['darren mcclennan', '1986 - 1997', '12', '43', '17 september 1986', '11 june 1997'], ['michael mcgarry', '1986 - 1997', '12', '54', '17 september 1986', 'australia 6 july 1997'], ['wynton rufer', '1980 - 1997', '12', '23', '16 october 1980', 'australia 28 june 1997'], ['steve wooddin', '1980 - 1984', '11', '24', '20 august 1980', '20 october 1984'], ['roy coxon', '1951 - 1952', '10', '8', 'new caledonia 19 september 1951', 'tahiti 28 september 1952'], ['chris jackson', '1995 - 2003', '10', '60', '21 february 1995', '22 june 2003'], ['dave taylor', '1967 - 1981', '10', '47', 'south vietnam 10 november 1967', '12 september 1981'], ['colin walker', '1984 - 1988', '10', '15', '18 october 1984', '23 june 1988'], ['chris wood', '2009 -', '10', '31', '3 june 2009', '5 september 2013']] |
upper grand district school board | https://en.wikipedia.org/wiki/Upper_Grand_District_School_Board | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1803594-1.html.csv | count | 4 schools in the upper grand district school board are located in guelph . | {'scope': 'all', 'criterion': 'equal', 'value': 'guelph', 'result': '4', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'guelph'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record fuzzily matches to guelph .', 'tostr': 'filter_eq { all_rows ; location ; guelph }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; location ; guelph } }', 'tointer': 'select the rows whose location record fuzzily matches to guelph . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; location ; guelph } } ; 4 } = true', 'tointer': 'select the rows whose location record fuzzily matches to guelph . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; location ; guelph } } ; 4 } = true | select the rows whose location record fuzzily matches to guelph . the number of such rows is 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'location_5': 5, 'guelph_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'location_5': 'location', 'guelph_6': 'guelph', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'location_5': [0], 'guelph_6': [0], '4_7': [2]} | ['name', 'location', 'enrollment', '1 - year ranking of 727', '5 - year ranking of 693'] | [['centennial collegiate vocational institute', 'guelph', '1533', '63', '22'], ['centre dufferin district high school', 'shelburne', '998', '265', '281'], ['centre wellington district high school', 'fergus', '1459', '330', '246'], ['college heights secondary school', 'guelph', '649', '717', '688'], ['erin district high school', 'erin', '616', '197', '148'], ['guelph collegiate vocational institute', 'guelph', '1314', '16', '30'], ['john f ross collegiate vocational institute', 'guelph', '1895', '181', '165'], ['norwell district secondary school', 'palmerston', '795', '126', '343'], ['orangeville district secondary school', 'orangeville', '1574', '181', '194'], ['wellington heights secondary school', 'mount forest', '680', '371', '426'], ['westside secondary school', 'orangeville', '996', '478', '343']] |
1905 in brazilian football | https://en.wikipedia.org/wiki/1905_in_Brazilian_football | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15421748-1.html.csv | ordinal | out of the brazilian football teams that played in 1905 , germnia had the second-highest amount of points . | {'row': '2', 'col': '3', 'order': '2', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'points', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; points ; 2 }'}, 'team'], 'result': 'germnia', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; points ; 2 } ; team }'}, 'germnia'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; points ; 2 } ; team } ; germnia } = true', 'tointer': 'select the row whose points record of all rows is 2nd maximum . the team record of this row is germnia .'} | eq { hop { nth_argmax { all_rows ; points ; 2 } ; team } ; germnia } = true | select the row whose points record of all rows is 2nd maximum . the team record of this row is germnia . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'points_5': 5, '2_6': 6, 'team_7': 7, 'germnia_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'points_5': 'points', '2_6': '2', 'team_7': 'team', 'germnia_8': 'germnia'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'points_5': [0], '2_6': [0], 'team_7': [1], 'germnia_8': [2]} | ['position', 'team', 'points', 'played', 'drawn', 'lost', 'against', 'difference'] | [['1', 'paulistano', '18', '10', '2', '0', '3', '17'], ['2', 'germnia', '13', '10', '3', '2', '16', '14'], ['3', 'sc internacional de são paulo', '11', '10', '3', '3', '19', '- 4'], ['4', 'são paulo athletic', '8', '10', '0', '6', '26', '- 10'], ['5', 'mackenzie', '7', '10', '1', '6', '27', '0'], ['6', 'aa das palmeiras', '3', '10', '1', '8', '27', '- 17']] |
lone star alliance | https://en.wikipedia.org/wiki/Lone_Star_Alliance | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-28243691-1.html.csv | majority | the majority of the members of the lone star alliance were founded before the year 1900 . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '1900', 'subset': None} | {'func': 'most_less', 'args': ['all_rows', 'founded', '1900'], 'result': True, 'ind': 0, 'tointer': 'for the founded records of all rows , most of them are less than 1900 .', 'tostr': 'most_less { all_rows ; founded ; 1900 } = true'} | most_less { all_rows ; founded ; 1900 } = true | for the founded records of all rows , most of them are less than 1900 . | 1 | 1 | {'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'founded_3': 3, '1900_4': 4} | {'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'founded_3': 'founded', '1900_4': '1900'} | {'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'founded_3': [0], '1900_4': [0]} | ['institution', 'location', 'founded', 'affiliation', 'enrollment', 'team nickname', 'primary conference'] | [['baylor university', 'waco , texas', '1845', 'private , baptist', '14769', 'bears', 'big 12 ( division i )'], ['university of louisiana at lafayette', 'lafayette , louisiana', '1898', 'public', '16361', "ragin ' cajuns", 'sunbelt ( division i )'], ['louisiana state university', 'baton rouge , louisiana', '1860', 'public', '25215', 'tigers', 'sec ( division i )'], ['university of north texas', 'denton , texas', '1890', 'public', '36206', 'mean green', 'c - usa ( division i )'], ['university of oklahoma', 'norman , oklahoma', '1890', 'public', '29931', 'sooners', 'big 12 ( division i )'], ['rice university', 'houston , texas', '1891', 'private / non - sectarian', '6799', 'owls', 'c - usa ( division i )'], ['southern methodist university', 'university park , texas', '1911', 'private / methodist', '10693', 'mustangs', 'american ( division i )'], ['texas a & m university', 'college station , texas', '1871', 'public', '48702', 'aggies', 'sec ( division i )'], ['texas christian university', 'fort worth , texas', '1873', 'private / disciples of christ', '8696', 'horned frogs', 'big 12 ( division i )'], ['texas state universitysan marcos', 'san marcos , texas', '1899', 'public', '32586', 'bobcats', 'sunbelt ( division i )'], ['texas tech university', 'lubbock , texas', '1923', 'public', '30049', 'red raiders', 'big 12 ( division i )'], ['university of texas at austin', 'austin , texas', '1883', 'public', '50995', 'longhorns', 'big 12 ( division i )']] |
lpga | https://en.wikipedia.org/wiki/LPGA | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-173345-6.html.csv | majority | most of the players in the top ten of the lpga have earned over 10,000,000 dollars . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '10000000', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'earnings', '10000000'], 'result': True, 'ind': 0, 'tointer': 'for the earnings records of all rows , most of them are greater than 10000000 .', 'tostr': 'most_greater { all_rows ; earnings ; 10000000 } = true'} | most_greater { all_rows ; earnings ; 10000000 } = true | for the earnings records of all rows , most of them are greater than 10000000 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'earnings_3': 3, '10000000_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'earnings_3': 'earnings', '10000000_4': '10000000'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'earnings_3': [0], '10000000_4': [0]} | ['rank', 'player', 'country', 'earned', 'earnings'] | [['1', 'annika sörenstam', 'sweden', '1993 - 2008', '22573192'], ['2', 'karrie webb', 'australia', '1995 - 2012', '17402218'], ['3', 'lorena ochoa', 'mexico', '2003 - 2010', '14863331'], ['4', 'cristie kerr', 'united states', '1997 - 2012', '14368457'], ['5', 'juli inkster', 'united states', '1983 - 2012', '13442946'], ['6', 'se ri pak', 'south korea', '1997 - 2012', '11815527'], ['7', 'paula creamer', 'united states', '2005 - 2012', '9594379'], ['8', 'suzann pettersen', 'norway', '2000 - 2012', '9368341'], ['9', 'meg mallon', 'united states', '1987 - 2010', '9051459'], ['10', 'yani tseng', 'taiwan', '2009 - 2012', '8971242']] |
2007 - 08 premier league | https://en.wikipedia.org/wiki/2007%E2%80%9308_Premier_League | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10592536-6.html.csv | unique | middlesbrough was the only team that used erreà as a kit maker . | {'scope': 'all', 'row': '13', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': 'erreà', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'kit maker', 'erreà'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose kit maker record fuzzily matches to erreà .', 'tostr': 'filter_eq { all_rows ; kit maker ; erreà }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; kit maker ; erreà } }', 'tointer': 'select the rows whose kit maker record fuzzily matches to erreà . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'kit maker', 'erreà'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose kit maker record fuzzily matches to erreà .', 'tostr': 'filter_eq { all_rows ; kit maker ; erreà }'}, 'team'], 'result': 'middlesbrough', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; kit maker ; erreà } ; team }'}, 'middlesbrough'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; kit maker ; erreà } ; team } ; middlesbrough }', 'tointer': 'the team record of this unqiue row is middlesbrough .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; kit maker ; erreà } } ; eq { hop { filter_eq { all_rows ; kit maker ; erreà } ; team } ; middlesbrough } } = true', 'tointer': 'select the rows whose kit maker record fuzzily matches to erreà . there is only one such row in the table . the team record of this unqiue row is middlesbrough .'} | and { only { filter_eq { all_rows ; kit maker ; erreà } } ; eq { hop { filter_eq { all_rows ; kit maker ; erreà } ; team } ; middlesbrough } } = true | select the rows whose kit maker record fuzzily matches to erreà . there is only one such row in the table . the team record of this unqiue row is middlesbrough . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'kit maker_7': 7, 'erreà_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'team_9': 9, 'middlesbrough_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'kit maker_7': 'kit maker', 'erreà_8': 'erreà', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'team_9': 'team', 'middlesbrough_10': 'middlesbrough'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'kit maker_7': [0], 'erreà_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'team_9': [2], 'middlesbrough_10': [3]} | ['team', 'manager', 'captain', 'kit maker', 'shirt sponsor'] | [['arsenal', 'arsène wenger', 'william gallas', 'nike', 'emirates'], ['aston villa', "martin o'neill", 'gareth barry', 'nike', '32red'], ['birmingham city', 'alex mcleish', 'damien johnson', 'umbro', 'f & c investments'], ['blackburn rovers', 'mark hughes', 'ryan nelsen', 'umbro', 'bet 24'], ['bolton wanderers', 'gary megson', 'kevin davies', 'reebok', 'reebok'], ['chelsea', 'avram grant', 'john terry', 'adidas', 'samsung mobile'], ['derby county', 'paul jewell', 'robbie savage', 'adidas', 'derbyshire building society'], ['everton', 'david moyes', 'phil neville', 'umbro', 'chang beer'], ['fulham', 'roy hodgson', 'brian mcbride', 'nike', 'lg'], ['liverpool', 'rafael benítez', 'steven gerrard', 'adidas', 'carlsberg'], ['manchester city', 'sven - goran eriksson', 'richard dunne', 'le coq sportif', 'thomas cookcom'], ['manchester united', 'sir alex ferguson', 'gary neville', 'nike', 'aig'], ['middlesbrough', 'gareth southgate', 'george boateng', 'erreà', 'garmin'], ['newcastle united', 'kevin keegan', 'nicky butt', 'adidas', 'northern rock'], ['portsmouth', 'harry redknapp', 'sol campbell', 'canterbury', 'oki'], ['reading', 'steve coppell', 'graeme murty', 'puma', 'kyocera'], ['sunderland', 'roy keane', 'dean whitehead', 'umbro', 'boylesportscom'], ['tottenham hotspur', 'juande ramos', 'ledley king', 'puma', 'mansion casino'], ['west ham united', 'alan curbishley', 'lucas neill', 'umbro', 'xl airways'], ['wigan athletic', 'steve bruce', 'mario melchiot', 'umbro', 'jjb sports']] |
australian region tropical cyclone climatology | https://en.wikipedia.org/wiki/Australian_region_tropical_cyclone_climatology | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14617522-3.html.csv | aggregation | there were a total of 59 severe tropical cyclones in the australian region in the 1990s . | {'scope': 'all', 'col': '4', 'type': 'sum', 'result': '59', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'severe tropical cyclones'], 'result': '59', 'ind': 0, 'tostr': 'sum { all_rows ; severe tropical cyclones }'}, '59'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; severe tropical cyclones } ; 59 } = true', 'tointer': 'the sum of the severe tropical cyclones record of all rows is 59 .'} | round_eq { sum { all_rows ; severe tropical cyclones } ; 59 } = true | the sum of the severe tropical cyclones record of all rows is 59 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'severe tropical cyclones_4': 4, '59_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'severe tropical cyclones_4': 'severe tropical cyclones', '59_5': '59'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'severe tropical cyclones_4': [0], '59_5': [1]} | ['season', 'tropical lows', 'tropical cyclones', 'severe tropical cyclones', 'strongest storm'] | [['1990 - 91', '10', '10', '7', 'marian'], ['1991 - 92', '11', '10', '9', 'jane - irna'], ['1992 - 93', '6', '3', '1', 'oliver'], ['1993 - 94', '12', '11', '7', 'theodore'], ['1994 - 95', '19', '9', '6', 'chloe'], ['1995 - 96', '19', '14', '9', 'olivia'], ['1996 - 97', '15', '14', '3', 'pancho'], ['1997 - 98', '10', '9', '3', 'tiffany'], ['1998 - 99', '21', '14', '9', 'gwenda'], ['1999 - 00', '13', '12', '5', 'john / paul']] |
maritime company of lesvos | https://en.wikipedia.org/wiki/Maritime_Company_of_Lesvos | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11570929-1.html.csv | ordinal | the second highest number of vessels in a ship in the maritime company of lesvos is attributed to the theofilos ship . | {'row': '4', 'col': '6', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'vessels', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; vessels ; 2 }'}, 'ship name'], 'result': 'theofilos', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; vessels ; 2 } ; ship name }'}, 'theofilos'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; vessels ; 2 } ; ship name } ; theofilos } = true', 'tointer': 'select the row whose vessels record of all rows is 2nd maximum . the ship name record of this row is theofilos .'} | eq { hop { nth_argmax { all_rows ; vessels ; 2 } ; ship name } ; theofilos } = true | select the row whose vessels record of all rows is 2nd maximum . the ship name record of this row is theofilos . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'vessels_5': 5, '2_6': 6, 'ship name_7': 7, 'theofilos_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'vessels_5': 'vessels', '2_6': '2', 'ship name_7': 'ship name', 'theofilos_8': 'theofilos'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'vessels_5': [0], '2_6': [0], 'ship name_7': [1], 'theofilos_8': [2]} | ['ship name', 'year', 'length', 'width', 'passengers', 'vessels', 'speed'] | [['mytilene', '1973', '138 , 3 m', '22 , 4 m', '1.730', '225', '20'], ['european express', '1974', '159 , 5 m', '21 , 5 m', '1.000', '350', '23'], ['ionian sky', '1974', '164 m', '24 m', '1.090', '600', '22'], ['theofilos', '1975', '149 , 4 m', '23 , 5 m', '1.660', '433', '18'], ['taxiarchis', '1976', '135 , 8 m', '20 , 6 m', '591', '392', '18'], ['aqua jewel', '2002', '108 m', '16 , 6 m', '1.675', '175', '18 , 5'], ['aqua maria', '1975', '101 , 3 m', '18 m', '592', '230', '17'], ['aqua spirit', '2000', '75 m', '15 m', '400', '60', '17']] |
list of south african provinces by population | https://en.wikipedia.org/wiki/List_of_South_African_provinces_by_population | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1860337-1.html.csv | majority | the majority of south african provinces have more than 4 million people . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '4000000', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'population ( 2011 )', '4000000'], 'result': True, 'ind': 0, 'tointer': 'for the population ( 2011 ) records of all rows , most of them are greater than 4000000 .', 'tostr': 'most_greater { all_rows ; population ( 2011 ) ; 4000000 } = true'} | most_greater { all_rows ; population ( 2011 ) ; 4000000 } = true | for the population ( 2011 ) records of all rows , most of them are greater than 4000000 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'population (2011)_3': 3, '4000000_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'population (2011)_3': 'population ( 2011 )', '4000000_4': '4000000'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'population (2011)_3': [0], '4000000_4': [0]} | ['rank', 'province', 'population ( 2011 )', 'percentage', 'population estimate ( 2013 )'] | [['1', 'gauteng', '12272263', '23.7', '12728400'], ['2', 'kwazulu - natal', '10267300', '19.8', '10456900'], ['3', 'eastern cape', '6562053', '12.7', '6620100'], ['4', 'western cape', '5822734', '11.2', '6016900'], ['5', 'limpopo', '5404868', '10.4', '5518000'], ['6', 'mpumalanga', '4039939', '7.8', '4128000'], ['7', 'north west', '3509953', '6.8', '3597600'], ['8', 'free state', '2745590', '5.3', '2753200'], ['9', 'northern cape', '1145861', '2.2', '1162900'], ['south africa', 'south africa', '51770561', '100.0', '52982000']] |
1997 - 98 manchester united f.c. season | https://en.wikipedia.org/wiki/1997%E2%80%9398_Manchester_United_F.C._season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13599021-7.html.csv | superlative | the match on 17 september 1997 had the lowest attendance of all the matches . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'attendance'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; attendance }'}, 'date'], 'result': '17 september 1997', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; attendance } ; date }'}, '17 september 1997'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; attendance } ; date } ; 17 september 1997 } = true', 'tointer': 'select the row whose attendance record of all rows is minimum . the date record of this row is 17 september 1997 .'} | eq { hop { argmin { all_rows ; attendance } ; date } ; 17 september 1997 } = true | select the row whose attendance record of all rows is minimum . the date record of this row is 17 september 1997 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'attendance_5': 5, 'date_6': 6, '17 september 1997_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', 'date_6': 'date', '17 september 1997_7': '17 september 1997'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'attendance_5': [0], 'date_6': [1], '17 september 1997_7': [2]} | ['date', 'opponents', 'result f - a', 'attendance', 'group position'] | [['17 september 1997', 'košice', '3 - 0', '9950', '2nd'], ['1 october 1997', 'juventus', '3 - 2', '53428', '1st'], ['22 october 1997', 'feyenoord', '2 - 1', '53188', '1st'], ['5 november 1997', 'feyenoord', '3 - 1', '51000', '1st'], ['27 november 1997', 'košice', '3 - 0', '53535', '1st'], ['10 december 1997', 'juventus', '0 - 1', '47786', '1st']] |
nathalie herreman | https://en.wikipedia.org/wiki/Nathalie_Herreman | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15097050-5.html.csv | count | in the final matches shown nathalie herreman and her partner finished as runner-up 3 times . | {'scope': 'all', 'criterion': 'equal', 'value': 'runner - up', 'result': '3', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'outcome', 'runner - up'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose outcome record fuzzily matches to runner - up .', 'tostr': 'filter_eq { all_rows ; outcome ; runner - up }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; outcome ; runner - up } }', 'tointer': 'select the rows whose outcome record fuzzily matches to runner - up . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; outcome ; runner - up } } ; 3 } = true', 'tointer': 'select the rows whose outcome record fuzzily matches to runner - up . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; outcome ; runner - up } } ; 3 } = true | select the rows whose outcome record fuzzily matches to runner - up . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'outcome_5': 5, 'runner - up_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'outcome_5': 'outcome', 'runner - up_6': 'runner - up', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'outcome_5': [0], 'runner - up_6': [0], '3_7': [2]} | ['outcome', 'date', 'tournament', 'surface', 'partner', 'opponents', 'score'] | [['winner', '1 november 1987', 'zurich , switzerland', 'carpet', 'pascale paradis', 'jana novotná arantxa catherine suire', '6 - 3 , 2 - 6 , 6 - 3'], ['winner', '24 july 1988', 'aix - en - provence , france', 'clay', 'catherine tanvier', 'sandra cecchini arantxa sánchez vicario', '6 - 4 , 7 - 5'], ['runner - up', '24 september 1989', 'paris , france', 'clay', 'catherine suire', 'sandra cecchini patricia tarabini', '1 - 6 , 1 - 6'], ['runner - up', '15 october 1989', 'moscow , ussr', 'carpet', 'catherine suire', 'larisa savchenko natalia zvereva', '3 - 6 , 4 - 6'], ['runner - up', '23 september 1990', 'paris , france', 'carpet', 'alexia dechaume', 'kristin godridge kirrily sharpe', '6 - 4 , 3 - 6 , 1 - 6']] |
2005 - 06 coventry city f.c. season | https://en.wikipedia.org/wiki/2005%E2%80%9306_Coventry_City_F.C._season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18884038-6.html.csv | count | in the 2005-06 coventry city f.c. season , 4 men won 1 league cup . | {'scope': 'all', 'criterion': 'equal', 'value': '1', 'result': '4', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'league cup', '1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose league cup record is equal to 1 .', 'tostr': 'filter_eq { all_rows ; league cup ; 1 }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; league cup ; 1 } }', 'tointer': 'select the rows whose league cup record is equal to 1 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; league cup ; 1 } } ; 4 } = true', 'tointer': 'select the rows whose league cup record is equal to 1 . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; league cup ; 1 } } ; 4 } = true | select the rows whose league cup record is equal to 1 . the number of such rows is 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'league cup_5': 5, '1_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'league cup_5': 'league cup', '1_6': '1', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'league cup_5': [0], '1_6': [0], '4_7': [2]} | ['name', 'championship', 'league cup', 'fa cup', 'total'] | [['gary mcsheffrey', '10', '1', '0', '11'], ['michael doyle', '9', '0', '0', '9'], ['richard duffy', '7', '0', '1', '8'], ['robert page', '8', '0', '0', '8'], ['dennis wise', '7', '0', '0', '7'], ['dele adebola', '4', '0', '1', '5'], ['don hutchison', '4', '0', '1', '5'], ['stern john', '4', '1', '0', '5'], ['marcus hall', '3', '1', '0', '4'], ['matt heath', '4', '0', '0', '4'], ['james scowcroft', '3', '0', '1', '4'], ['adrian williams', '4', '0', '0', '4'], ['stephen hughes', '2', '0', '1', '3'], ['richard shaw', '3', '0', '0', '3'], ['willo flood', '2', '0', '0', '2'], ['claus bech jãrgensen', '2', '0', '0', '2'], ['isaac osbourne', '1', '1', '0', '2'], ['kevin thornton', '2', '0', '0', '2'], ['andrew whing', '2', '0', '0', '2']] |
mona - jeanette berntsen | https://en.wikipedia.org/wiki/Mona-Jeanette_Berntsen | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18615911-1.html.csv | unique | week 5 was the only week that had an injury result in a dance by mona - jeanette berntsen . | {'scope': 'all', 'row': '6', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'injured', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'injured'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to injured .', 'tostr': 'filter_eq { all_rows ; result ; injured }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; result ; injured } }', 'tointer': 'select the rows whose result record fuzzily matches to injured . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'injured'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to injured .', 'tostr': 'filter_eq { all_rows ; result ; injured }'}, 'week'], 'result': '5', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; result ; injured } ; week }'}, '5'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; result ; injured } ; week } ; 5 }', 'tointer': 'the week record of this unqiue row is 5 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; result ; injured } } ; eq { hop { filter_eq { all_rows ; result ; injured } ; week } ; 5 } } = true', 'tointer': 'select the rows whose result record fuzzily matches to injured . there is only one such row in the table . the week record of this unqiue row is 5 .'} | and { only { filter_eq { all_rows ; result ; injured } } ; eq { hop { filter_eq { all_rows ; result ; injured } ; week } ; 5 } } = true | select the rows whose result record fuzzily matches to injured . there is only one such row in the table . the week record of this unqiue row is 5 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'result_7': 7, 'injured_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'week_9': 9, '5_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'result_7': 'result', 'injured_8': 'injured', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'week_9': 'week', '5_10': '5'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'result_7': [0], 'injured_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'week_9': [2], '5_10': [3]} | ['week', 'partner', 'dance', 'music', 'result'] | [['1', 'endre jansen', 'afro', "wan na be startin ' somethin' - michael jackson", 'safe'], ['2', 'endre jansen', 'lyrical jazz', "hangin ' by a thread - jann arden", 'safe'], ['3', 'endre jansen', 'locking', 'rock steady - aretha franklin', 'bottom 3'], ['3', 'results show solo', 'results show solo', 'ring the alarm - beyoncé knowles', 'bottom 3'], ['4', 'endre jansen', 'jive', 'bye bye-david cerra', 'safe'], ['5', 'ole petter knarvik', 'hip - hop', 'ice box - omarion', 'injured']] |
list of lancashire county cricket club records | https://en.wikipedia.org/wiki/List_of_Lancashire_County_Cricket_Club_records | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14176339-8.html.csv | majority | most of the records set by the leicester cricket club were set after 1900 . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '1900', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'year', '1900'], 'result': True, 'ind': 0, 'tointer': 'for the year records of all rows , most of them are greater than 1900 .', 'tostr': 'most_greater { all_rows ; year ; 1900 } = true'} | most_greater { all_rows ; year ; 1900 } = true | for the year records of all rows , most of them are greater than 1900 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'year_3': 3, '1900_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'year_3': 'year', '1900_4': '1900'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'year_3': [0], '1900_4': [0]} | ['score', 'opposition', 'venue', 'city', 'year'] | [['1 run', 'leicestershire', 'aylestone road', 'leicester', '1906'], ['1 runs', 'hampshire', 'aigburth', 'liverpool', '1920'], ['2 runs', 'leicestershire', 'aylestone road', 'leicester', '1922'], ['3 runs', 'yorkshire', 'fartown', 'huddersfield', '1889'], ['3 runs', 'derbyshire', 'park road ground', 'buxton', '1947']] |
united states women 's national water polo team | https://en.wikipedia.org/wiki/United_States_women%27s_national_water_polo_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16506555-1.html.csv | unique | maggie steffens is the only player on the united states women 's national water polo team from the diablo water polo club . | {'scope': 'all', 'row': '6', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'diablo water polo', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '2012 club', 'diablo water polo'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 2012 club record fuzzily matches to diablo water polo .', 'tostr': 'filter_eq { all_rows ; 2012 club ; diablo water polo }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; 2012 club ; diablo water polo } }', 'tointer': 'select the rows whose 2012 club record fuzzily matches to diablo water polo . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '2012 club', 'diablo water polo'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 2012 club record fuzzily matches to diablo water polo .', 'tostr': 'filter_eq { all_rows ; 2012 club ; diablo water polo }'}, 'name'], 'result': 'maggie steffens', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; 2012 club ; diablo water polo } ; name }'}, 'maggie steffens'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; 2012 club ; diablo water polo } ; name } ; maggie steffens }', 'tointer': 'the name record of this unqiue row is maggie steffens .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; 2012 club ; diablo water polo } } ; eq { hop { filter_eq { all_rows ; 2012 club ; diablo water polo } ; name } ; maggie steffens } } = true', 'tointer': 'select the rows whose 2012 club record fuzzily matches to diablo water polo . there is only one such row in the table . the name record of this unqiue row is maggie steffens .'} | and { only { filter_eq { all_rows ; 2012 club ; diablo water polo } } ; eq { hop { filter_eq { all_rows ; 2012 club ; diablo water polo } ; name } ; maggie steffens } } = true | select the rows whose 2012 club record fuzzily matches to diablo water polo . there is only one such row in the table . the name record of this unqiue row is maggie steffens . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, '2012 club_7': 7, 'diablo water polo_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'maggie steffens_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', '2012 club_7': '2012 club', 'diablo water polo_8': 'diablo water polo', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'maggie steffens_10': 'maggie steffens'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], '2012 club_7': [0], 'diablo water polo_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'maggie steffens_10': [3]} | ['name', 'pos', 'height', 'weight', '2012 club'] | [['elizabeth armstrong', 'gk', 'm', '-', 'great lakes wp club'], ['heather petri', 'd', 'm', '-', 'new york athletic club'], ['melissa seidemann', 'cb', 'm', '-', 'stanford university'], ['brenda villa', 'd', 'm', '-', 'orizzonte catania'], ['lauren wenger', 'd', 'm', '-', 'new york athletic club'], ['maggie steffens', 'cb', 'm', '-', 'diablo water polo'], ['courtney mathewson', 'd', 'm', '-', 'new york athletic club'], ['jessica steffens', 'cb', 'm', '-', 'new york athletic club'], ['elsie windes', 'cb', 'm', '-', 'tualatin hills wpc'], ['kelly rulon', 'd', 'm', '-', 'asd roma'], ['annika dries', 'cf', 'm', '-', 'stanford university'], ['kami craig', 'cf', 'm', '-', 'santa barbara wp foundation'], ['tumua anae', 'gk', 'm', '-', 'socal']] |
2008 spanish motorcycle grand prix | https://en.wikipedia.org/wiki/2008_Spanish_motorcycle_Grand_Prix | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16193157-1.html.csv | majority | most of the riders finished all 27 laps of the grand prix . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': '27', 'subset': None} | {'func': 'most_eq', 'args': ['all_rows', 'laps', '27'], 'result': True, 'ind': 0, 'tointer': 'for the laps records of all rows , most of them are equal to 27 .', 'tostr': 'most_eq { all_rows ; laps ; 27 } = true'} | most_eq { all_rows ; laps ; 27 } = true | for the laps records of all rows , most of them are equal to 27 . | 1 | 1 | {'most_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'laps_3': 3, '27_4': 4} | {'most_eq_0': 'most_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'laps_3': 'laps', '27_4': '27'} | {'most_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'laps_3': [0], '27_4': [0]} | ['rider', 'manufacturer', 'laps', 'time', 'grid'] | [['dani pedrosa', 'honda', '27', '45:35:121', '2'], ['valentino rossi', 'yamaha', '27', '+ 2.883', '5'], ['jorge lorenzo', 'yamaha', '27', '+ 4.339', '1'], ['nicky hayden', 'honda', '27', '+ 10.142', '4'], ['loris capirossi', 'suzuki', '27', '+ 27.524', '10'], ['james toseland', 'yamaha', '27', '+ 27.808', '8'], ['john hopkins', 'kawasaki', '27', '+ 28.296', '9'], ['andrea dovizioso', 'honda', '27', '+ 28.449', '13'], ['shinya nakano', 'honda', '27', '+ 32.569', '11'], ['chris vermeulen', 'suzuki', '27', '+ 35.091', '12'], ['casey stoner', 'ducati', '27', '+ 42.223', '7'], ['marco melandri', 'ducati', '27', '+ 44.498', '18'], ['anthony west', 'kawasaki', '27', '+ 45.807', '15'], ['alex de angelis', 'honda', '27', '+ 45.871', '14'], ['toni elias', 'ducati', '27', '+ 1:09.558', '16'], ['sylvain guintoli', 'ducati', '27', '+ 1:14.442', '17'], ['colin edwards', 'yamaha', '5', 'accident', '3'], ['randy de puniet', 'honda', '2', 'retirement', '6']] |
50 metre running target mixed | https://en.wikipedia.org/wiki/50_metre_running_target_mixed | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18938213-1.html.csv | count | jerzy greszkiewicz won two bronze medals in the 50 metre running target mixed . | {'scope': 'all', 'criterion': 'equal', 'value': 'jerzy greszkiewicz', 'result': '2', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'bronze', 'jerzy greszkiewicz'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose bronze record fuzzily matches to jerzy greszkiewicz .', 'tostr': 'filter_eq { all_rows ; bronze ; jerzy greszkiewicz }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; bronze ; jerzy greszkiewicz } }', 'tointer': 'select the rows whose bronze record fuzzily matches to jerzy greszkiewicz . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; bronze ; jerzy greszkiewicz } } ; 2 } = true', 'tointer': 'select the rows whose bronze record fuzzily matches to jerzy greszkiewicz . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; bronze ; jerzy greszkiewicz } } ; 2 } = true | select the rows whose bronze record fuzzily matches to jerzy greszkiewicz . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'bronze_5': 5, 'jerzy greszkiewicz_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'bronze_5': 'bronze', 'jerzy greszkiewicz_6': 'jerzy greszkiewicz', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'bronze_5': [0], 'jerzy greszkiewicz_6': [0], '2_7': [2]} | ['year', 'place', 'gold', 'silver', 'bronze'] | [['1970', 'phoenix', 'peter cheng ( hkg )', 'valeri postoianov ( urs )', 'jogan nikitin ( urs )'], ['1973', 'stockport', 'peter cheng ( hkg )', 'alexander kediarov ( urs )', 'helmut bellingrodt ( col )'], ['1974', 'berne', 'peter cheng ( hkg )', 'alexander kediarov ( urs )', 'alexander gazov ( urs )'], ['1978', 'seoul', 'peter cheng ( hkg )', 'guenther danne ( frg )', 'ezio cini ( ita )'], ['1979', 'linz', 'peter cheng ( hkg )', 'igor sokolov ( urs )', 'gyula szabo ( hun )'], ['1981', 'mala', 'aleksei rudnizkiy ( urs )', 'alexander ivanchikhin ( urs )', 'tibor bodnar ( hun )'], ['1982', 'caracas', 'nikolai dedov ( urs )', 'yuri kadenatsy ( urs )', 'jerzy greszkiewicz ( pol )'], ['1983', 'edmonton', 'sergei savostianov ( urs )', 'nikolai dedov ( urs )', 'jerzy greszkiewicz ( pol )'], ['1986', 'suhl', 'attila solti ( hun )', 'shiping huang ( chn )', 'yuwei li ( chn )'], ['1990', 'moscow', 'ronghui zhang ( chn )', 'gennadi avramenko ( urs )', 'manfred kurzer ( gdr )'], ['1994', 'milan', 'lubos racansky ( cze )', 'gennadi avramenko ( ukr )', 'miroslav janus ( cze )'], ['2002', 'lahti', 'jozsef sike ( hun )', 'emil andersson ( swe )', 'lubomir pelach ( svk )'], ['2006', 'zagreb', 'lukasz czapla ( pol )', 'peter pelach ( svk )', 'bedrich jonas ( cze )'], ['2008', 'plzeň', 'alexander blinov ( rus )', 'peter pelach ( svk )', 'alexander zinenko ( ukr )'], ['2009', 'heinola', 'peter cheng ( hkg )', 'staffan holmström ( fin )', 'niklas bergström ( swe )']] |
rté radio | https://en.wikipedia.org/wiki/RT%C3%89_Radio | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18475946-2.html.csv | comparative | the frequency of rté radio 's 2fm channel is higher in the transmitter covering southwestern ireland , compared to that covering the southeast . | {'row_1': '6', 'row_2': '5', 'col': '4', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'service area', 'sw ireland'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose service area record fuzzily matches to sw ireland .', 'tostr': 'filter_eq { all_rows ; service area ; sw ireland }'}, '2fm ( mhz )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; service area ; sw ireland } ; 2fm ( mhz ) }', 'tointer': 'select the rows whose service area record fuzzily matches to sw ireland . take the 2fm ( mhz ) record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'service area', 'se ireland'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose service area record fuzzily matches to se ireland .', 'tostr': 'filter_eq { all_rows ; service area ; se ireland }'}, '2fm ( mhz )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; service area ; se ireland } ; 2fm ( mhz ) }', 'tointer': 'select the rows whose service area record fuzzily matches to se ireland . take the 2fm ( mhz ) record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; service area ; sw ireland } ; 2fm ( mhz ) } ; hop { filter_eq { all_rows ; service area ; se ireland } ; 2fm ( mhz ) } } = true', 'tointer': 'select the rows whose service area record fuzzily matches to sw ireland . take the 2fm ( mhz ) record of this row . select the rows whose service area record fuzzily matches to se ireland . take the 2fm ( mhz ) record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; service area ; sw ireland } ; 2fm ( mhz ) } ; hop { filter_eq { all_rows ; service area ; se ireland } ; 2fm ( mhz ) } } = true | select the rows whose service area record fuzzily matches to sw ireland . take the 2fm ( mhz ) record of this row . select the rows whose service area record fuzzily matches to se ireland . take the 2fm ( mhz ) record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'service area_7': 7, 'sw ireland_8': 8, '2fm (mhz)_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'service area_11': 11, 'se ireland_12': 12, '2fm (mhz)_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'service area_7': 'service area', 'sw ireland_8': 'sw ireland', '2fm (mhz)_9': '2fm ( mhz )', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'service area_11': 'service area', 'se ireland_12': 'se ireland', '2fm (mhz)_13': '2fm ( mhz )'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'service area_7': [0], 'sw ireland_8': [0], '2fm (mhz)_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'service area_11': [1], 'se ireland_12': [1], '2fm (mhz)_13': [3]} | ['transmitter', 'service area', 'radio 1 ( mhz )', '2fm ( mhz )', 'rnag ( mhz )', 'lyric fm ( mhz )', 'erp ( kw )'] | [['cairn hill', 'the midlands', '89.8', 'n / a', 'n / a', 'n / a', '16'], ['clermont carn', 'ne ireland , northern ireland', '87.8', '97.0', '102.7', '95.2', '40'], ['kippure', 'dublin , wicklow , se midlands', '89.1', '91.3', '93.5', '98.7', '40'], ['maghera', 'west ireland', '88.8', '91.0', '93.2', '98.4', '160'], ['mount leinster', 'se ireland', '89.6', '91.8', '94.0', '99.2', '100'], ['mullaghanish', 'sw ireland', '90.0', '92.2', '94.4', '99.6', '160'], ['three rock', 'dublin city and county', '88.5', '90.7', '92.9', '96.7', '12.5']] |
2008 north west 200 races | https://en.wikipedia.org/wiki/2008_North_West_200_Races | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17477518-2.html.csv | comparative | steve plater had a higher speed in the 2008 north west 200 races compared to denver robb . | {'row_1': '4', 'row_2': '9', 'col': '5', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'rider', 'steve plater'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose rider record fuzzily matches to steve plater .', 'tostr': 'filter_eq { all_rows ; rider ; steve plater }'}, 'speed'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; rider ; steve plater } ; speed }', 'tointer': 'select the rows whose rider record fuzzily matches to steve plater . take the speed record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'rider', 'denver robb'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose rider record fuzzily matches to denver robb .', 'tostr': 'filter_eq { all_rows ; rider ; denver robb }'}, 'speed'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; rider ; denver robb } ; speed }', 'tointer': 'select the rows whose rider record fuzzily matches to denver robb . take the speed record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; rider ; steve plater } ; speed } ; hop { filter_eq { all_rows ; rider ; denver robb } ; speed } } = true', 'tointer': 'select the rows whose rider record fuzzily matches to steve plater . take the speed record of this row . select the rows whose rider record fuzzily matches to denver robb . take the speed record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; rider ; steve plater } ; speed } ; hop { filter_eq { all_rows ; rider ; denver robb } ; speed } } = true | select the rows whose rider record fuzzily matches to steve plater . take the speed record of this row . select the rows whose rider record fuzzily matches to denver robb . take the speed record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'rider_7': 7, 'steve plater_8': 8, 'speed_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'rider_11': 11, 'denver robb_12': 12, 'speed_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'rider_7': 'rider', 'steve plater_8': 'steve plater', 'speed_9': 'speed', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'rider_11': 'rider', 'denver robb_12': 'denver robb', 'speed_13': 'speed'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'rider_7': [0], 'steve plater_8': [0], 'speed_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'rider_11': [1], 'denver robb_12': [1], 'speed_13': [3]} | ['rank', 'rider', 'team', 'time', 'speed'] | [['1', 'michael rutter', 'ducati', "21 ' 52.169", '122.609 mph'], ['2', 'guy martin', 'honda', '+ 0.810', '122.534 mph'], ['3', 'john mcguinness', 'honda', '+ 0.956', '122.510 mph'], ['4', 'steve plater', 'yamaha yzf - r', '+ 1.192', '121.658 mph'], ['5', 'gary johnson', 'honda', '+ 10.257', '120.979 mph'], ['6', 'bruce anstey', 'suzuki gsx - r1000', '+ 17.682', '120.979 mph'], ['7', 'ian hutchinson', 'yamaha', '+ 17.970', '120.953 mph'], ['8', 'ryan farquhar', 'kawasaki zx - 10r', '+ 18.386', '120.915 mph'], ['9', 'denver robb', 'suzuki', '+ 27.118', '120.127 mph'], ['10', 'keith amor', 'honda', '+ 41.841', '118.820 mph']] |
high - temperature superconductivity | https://en.wikipedia.org/wiki/High-temperature_superconductivity | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-101336-1.html.csv | majority | the majority of high - temperature superconductivity compounds have a tetragonal crystal structure . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'tetragonal', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'crystal structure', 'tetragonal'], 'result': True, 'ind': 0, 'tointer': 'for the crystal structure records of all rows , most of them fuzzily match to tetragonal .', 'tostr': 'most_eq { all_rows ; crystal structure ; tetragonal } = true'} | most_eq { all_rows ; crystal structure ; tetragonal } = true | for the crystal structure records of all rows , most of them fuzzily match to tetragonal . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'crystal structure_3': 3, 'tetragonal_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'crystal structure_3': 'crystal structure', 'tetragonal_4': 'tetragonal'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'crystal structure_3': [0], 'tetragonal_4': [0]} | ['formula', 'notation', 't c ( k )', 'no of cu - o planes in unit cell', 'crystal structure'] | [['yba 2 cu 3 o 7', '123', '92', '2', 'orthorhombic'], ['bi 2 sr 2 cuo 6', 'bi - 2201', '20', '1', 'tetragonal'], ['bi 2 sr 2 cacu 2 o 8', 'bi - 2212', '85', '2', 'tetragonal'], ['bi 2 sr 2 ca 2 cu 3 o 6', 'bi - 2223', '110', '3', 'tetragonal'], ['tl 2 ba 2 cuo 6', 'tl - 2201', '80', '1', 'tetragonal'], ['tl 2 ba 2 cacu 2 o 8', 'tl - 2212', '108', '2', 'tetragonal'], ['tl 2 ba 2 ca 2 cu 3 o 10', 'tl - 2223', '125', '3', 'tetragonal'], ['tlba 2 ca 3 cu 4 o 11', 'tl - 1234', '122', '4', 'tetragonal'], ['hgba 2 cuo 4', 'hg - 1201', '94', '1', 'tetragonal'], ['hgba 2 cacu 2 o 6', 'hg - 1212', '128', '2', 'tetragonal'], ['hgba 2 ca 2 cu 3 o 8', 'hg - 1223', '134', '3', 'tetragonal']] |
boroughs of sherbrooke | https://en.wikipedia.org/wiki/Boroughs_of_Sherbrooke | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14927794-1.html.csv | count | three of the boroughs of sherbrooke have four borough councilors . | {'scope': 'all', 'criterion': 'equal', 'value': '4', 'result': '3', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'number of borough councilors', '4'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose number of borough councilors record is equal to 4 .', 'tostr': 'filter_eq { all_rows ; number of borough councilors ; 4 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; number of borough councilors ; 4 } }', 'tointer': 'select the rows whose number of borough councilors record is equal to 4 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; number of borough councilors ; 4 } } ; 3 } = true', 'tointer': 'select the rows whose number of borough councilors record is equal to 4 . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; number of borough councilors ; 4 } } ; 3 } = true | select the rows whose number of borough councilors record is equal to 4 . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'number of borough councilors_5': 5, '4_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'number of borough councilors_5': 'number of borough councilors', '4_6': '4', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'number of borough councilors_5': [0], '4_6': [0], '3_7': [2]} | ['borough', 'components', 'population', 'number of borough councilors', 'number of municipal councilors'] | [['brompton', 'bromptonville', '5771', '3', '1'], ['fleurimont', 'eastern sherbrooke , fleurimont', '41289', '5', '5'], ['lennoxville', 'lennoxville', '4947', '3', '1'], ['mont - bellevue', 'western sherbrooke , ascot', '31373', '4', '4'], ['rock forest - saint - élie - deauville', "rock forest , saint - élie - d'orford , deauville", '26757', '4', '4'], ['jacques - cartier', 'northern sherbrooke', '29311', '4', '4']] |
2010 ucla bruins baseball team | https://en.wikipedia.org/wiki/2010_UCLA_Bruins_baseball_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27862483-4.html.csv | count | the 2010 ucla bruins played three games against usc in the month of may . | {'scope': 'all', 'criterion': 'equal', 'value': 'usc', 'result': '3', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'usc'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to usc .', 'tostr': 'filter_eq { all_rows ; opponent ; usc }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; opponent ; usc } }', 'tointer': 'select the rows whose opponent record fuzzily matches to usc . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; opponent ; usc } } ; 3 } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to usc . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; opponent ; usc } } ; 3 } = true | select the rows whose opponent record fuzzily matches to usc . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'opponent_5': 5, 'usc_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'opponent_5': 'opponent', 'usc_6': 'usc', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'opponent_5': [0], 'usc_6': [0], '3_7': [2]} | ['', 'date', 'opponent', 'site / stadium', 'score', 'win', 'loss', 'save', 'attendance', 'overall record', 'pac - 10 record'] | [['39', 'may 1', 'arizona state', 'jackie robinson stadium', '6 - 1', 'm kelly ( 9 - 0 )', 't bauer ( 6 - 3 )', 'b rodgers ( 3 )', '1725', '30 - 9', '7 - 7'], ['40', 'may 2', 'arizona state', 'jackie robinson stadium', '12 - 3', 'j borup ( 9 - 1 )', 'r rasmussen ( 6 - 2 )', 'none', '1921', '30 - 10', '7 - 8'], ['41', 'may 4', 'pepperdine', 'eddy d field stadium', '5 - 1', 'g claypool ( 7 - 1 )', 'r dickmann ( 6 - 4 )', 'none', '261', '31 - 10', '7 - 8'], ['42', 'may 7', 'washington', 'husky ballpark', '7 - 2', 'g cole ( 7 - 2 )', 'g brown ( 1 - 4 )', 'none', '485', '32 - 10', '8 - 8'], ['43', 'may 8', 'washington', 'husky ballpark', '14 - 6', 't bauer ( 7 - 3 )', 'a kittredge ( 6 - 4 )', 'd klein ( 9 )', '716', '33 - 10', '9 - 8'], ['44', 'may 9', 'washington', 'husky ballpark', '7 - 6', 'r rasmussen ( 7 - 2 )', 'f snow ( 4 - 2 )', 'none', '562', '34 - 10', '10 - 8'], ['45', 'may 11', 'uc irvine', 'cicerone field', '2 - 1', 'n hoover ( 2 - 0 )', 'g claypool ( 7 - 2 )', 'e brock ( 1 )', '1172', '34 - 11', '10 - 8'], ['46', 'may 14', 'usc', 'jackie robinson stadium', '13 - 7', 'g cole ( 8 - 2 )', 'b mount ( 5 - 4 )', 'none', '1707', '35 - 11', '11 - 8'], ['47', 'may 15', 'usc', 'jackie robinson stadium', '15 - 2', 't bauer ( 8 - 3 )', 'c mezger ( 4 - 1 )', 'none', '1360', '36 - 11', '12 - 8'], ['48', 'may 16', 'usc', 'jackie robinson stadium', '2 - 1', 'd klein ( 4 - 0 )', 'c smith ( 4 - 6 )', 'none', '1531', '37 - 11', '13 - 8'], ['49', 'may 18', 'uc santa barbara', 'jackie robinson stadium', '6 - 2', 'g claypool ( 8 - 2 )', 'n capito ( 4 - 6 )', 'none', '587', '38 - 11', '13 - 8'], ['50', 'may 21', 'california', 'evans diamond', '8 - 7', 'd klein ( 5 - 0 )', 'm flemer ( 2 - 3 )', 'none', '417', '39 - 11', '14 - 8'], ['51', 'may 22', 'california', 'evans diamond', '12 - 4', 't bauer ( 9 - 3 )', 'd anderson ( 4 - 3 )', 'none', '534', '40 - 11', '15 - 8'], ['52', 'may 23', 'california', 'evans diamond', '11 - 2', 'r rasmussen ( 8 - 2 )', 'j jones ( 9 - 5 )', 'none', '737', '41 - 11', '16 - 8'], ['53', 'may 25', 'cal state fullerton', 'goodwin field', '5 - 2', 'no ramirez ( 9 - 1 )', 'g claypool ( 8 - 3 )', 'ni ramirez ( 9 )', '2376', '41 - 12', '16 - 8'], ['54', 'may 28', 'washington state', 'jackie robinson stadium', '6 - 1', 'g cole ( 9 - 2 )', 'c arnold ( 5 - 3 )', 'none', '1006', '42 - 12', '17 - 8'], ['55', 'may 29', 'washington state', 'jackie robinson stadium', '6 - 4', 's harvey ( 3 - 1 )', 'm grace ( 0 - 1 )', 'a conley ( 11 )', '1170', '42 - 13', '17 - 9']] |
cultural interest fraternities and sororities | https://en.wikipedia.org/wiki/Cultural_interest_fraternities_and_sororities | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2538117-7.html.csv | count | four of the organizations are classified as fraternities . | {'scope': 'all', 'criterion': 'equal', 'value': 'fraternity', 'result': '4', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'type', 'fraternity'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose type record fuzzily matches to fraternity .', 'tostr': 'filter_eq { all_rows ; type ; fraternity }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; type ; fraternity } }', 'tointer': 'select the rows whose type record fuzzily matches to fraternity . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; type ; fraternity } } ; 4 } = true', 'tointer': 'select the rows whose type record fuzzily matches to fraternity . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; type ; fraternity } } ; 4 } = true | select the rows whose type record fuzzily matches to fraternity . the number of such rows is 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'type_5': 5, 'fraternity_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'type_5': 'type', 'fraternity_6': 'fraternity', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'type_5': [0], 'fraternity_6': [0], '4_7': [2]} | ['letters', 'organization', 'nickname', 'founding date', 'founding university', 'type'] | [['δλφ', 'delta lambda phi', "dlp , deltas ' , or lambda men", '1986 - 10 - 15', 'washington , dc', 'fraternity'], ['κψκ', 'kappa psi kappa', 'canes , k - psis , diamonds , or angels', '2001 - 08 - 17', 'tallahassee , florida', 'fraternity'], ['οεπ', 'omicron epsilon pi', 'the epps', '2000 - 12 - 07', 'tallahassee , florida', 'sorority'], ['γρλ', 'gamma rho lambda 1', 'grl', '2003 - 08 - 25', 'tempe , arizona', 'sorority'], ['αλζ', 'alpha lambda zeta', 'the regal alphas', '2006 - 01 - 09', 'houston , texas and atlanta , georgia', 'fraternity'], ['καλ', 'kappa alpha lambda', 'the kappas', '2003 - 10 - 19', 'clark atlanta university', 'sorority'], ['φαν', 'phi alpha nu', 'phi - nomenal gentlewomen', '2006 - 07 - 30', 'charlotte , nc', 'fraternity']] |
1956 - 57 new york rangers season | https://en.wikipedia.org/wiki/1956%E2%80%9357_New_York_Rangers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17323267-7.html.csv | count | the new york rangers played against the toronto maple leafs three times . | {'scope': 'all', 'criterion': 'equal', 'value': 'toronto maple leafs', 'result': '3', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'toronto maple leafs'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to toronto maple leafs .', 'tostr': 'filter_eq { all_rows ; opponent ; toronto maple leafs }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; opponent ; toronto maple leafs } }', 'tointer': 'select the rows whose opponent record fuzzily matches to toronto maple leafs . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; opponent ; toronto maple leafs } } ; 3 } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to toronto maple leafs . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; opponent ; toronto maple leafs } } ; 3 } = true | select the rows whose opponent record fuzzily matches to toronto maple leafs . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'opponent_5': 5, 'toronto maple leafs_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'opponent_5': 'opponent', 'toronto maple leafs_6': 'toronto maple leafs', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'opponent_5': [0], 'toronto maple leafs_6': [0], '3_7': [2]} | ['game', 'march', 'opponent', 'score', 'record'] | [['61', '2', 'boston bruins', '3 - 2', '23 - 27 - 11'], ['62', '3', 'detroit red wings', '1 - 1', '23 - 27 - 12'], ['63', '7', 'chicago black hawks', '2 - 2', '23 - 27 - 13'], ['64', '9', 'toronto maple leafs', '2 - 1', '24 - 27 - 13'], ['65', '10', 'detroit red wings', '4 - 1', '25 - 27 - 13'], ['66', '13', 'boston bruins', '2 - 1', '25 - 28 - 13'], ['67', '16', 'toronto maple leafs', '14 - 1', '25 - 29 - 13'], ['68', '17', 'toronto maple leafs', '5 - 3', '25 - 30 - 13'], ['69', '23', 'boston bruins', '4 - 2', '26 - 30 - 13'], ['70', '24', 'chicago black hawks', '4 - 4', '26 - 30 - 14']] |
1987 pittsburgh gladiators season | https://en.wikipedia.org/wiki/1987_Pittsburgh_Gladiators_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11938731-7.html.csv | superlative | craig walls was the player who recorded the highest number of sacks during the 1987 pittsburgh gladiators season . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '2', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'sack'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; sack }'}, 'player'], 'result': 'craig walls', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; sack } ; player }'}, 'craig walls'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; sack } ; player } ; craig walls } = true', 'tointer': 'select the row whose sack record of all rows is maximum . the player record of this row is craig walls .'} | eq { hop { argmax { all_rows ; sack } ; player } ; craig walls } = true | select the row whose sack record of all rows is maximum . the player record of this row is craig walls . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'sack_5': 5, 'player_6': 6, 'craig walls_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'sack_5': 'sack', 'player_6': 'player', 'craig walls_7': 'craig walls'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'sack_5': [0], 'player_6': [1], 'craig walls_7': [2]} | ['player', 'tackles', 'solo', 'assisted', 'sack', 'yards', "td 's"] | [['joel gueli', '31', '29', '4', '3', '31', '1'], ['craig walls', '19', '15', '8', '13', '0', '0'], ['russell hairston', '17.5', '16', '0', '0', '50', '1'], ['creig federico', '17', '12', '10', '3', '0', '0'], ['scott dmitrenko', '15', '13', '4', '3', '0', '0'], ['mike stoops', '14.5', '11', '7', '0', '0', '0'], ['john mcclennon', '12.5', '9', '7', '0', '5', '0'], ['ricky mitchell', '11', '10', '2', '2', '0', '0'], ['jim rafferty', '10.5', '8', '2', '0', '4', '0'], ['thomas weaver', '9', '7', '4', '3', '2', '0'], ['earnest adams', '8', '6', '4', '5', '0', '0'], ['mike powell', '6', '5', '2', '0', '0', '0'], ['greg best', '6', '6', '0', '0', '0', '0'], ['willis yates', '5', '4', '2', '6', '0', '0'], ['lee larsen', '2.5', '2', '1', '0', '0', '0']] |
2007 fedex cup playoffs | https://en.wikipedia.org/wiki/2007_FedEx_Cup_Playoffs | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-13282157-1.html.csv | comparative | kj choi earned more reset points than charles howell iii earned . | {'row_1': '5', 'row_2': '8', 'col': '6', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'kj choi'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to kj choi .', 'tostr': 'filter_eq { all_rows ; player ; kj choi }'}, 'reset points'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; kj choi } ; reset points }', 'tointer': 'select the rows whose player record fuzzily matches to kj choi . take the reset points record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'charles howell iii'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to charles howell iii .', 'tostr': 'filter_eq { all_rows ; player ; charles howell iii }'}, 'reset points'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; charles howell iii } ; reset points }', 'tointer': 'select the rows whose player record fuzzily matches to charles howell iii . take the reset points record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; player ; kj choi } ; reset points } ; hop { filter_eq { all_rows ; player ; charles howell iii } ; reset points } } = true', 'tointer': 'select the rows whose player record fuzzily matches to kj choi . take the reset points record of this row . select the rows whose player record fuzzily matches to charles howell iii . take the reset points record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; player ; kj choi } ; reset points } ; hop { filter_eq { all_rows ; player ; charles howell iii } ; reset points } } = true | select the rows whose player record fuzzily matches to kj choi . take the reset points record of this row . select the rows whose player record fuzzily matches to charles howell iii . take the reset points record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'player_7': 7, 'kj choi_8': 8, 'reset points_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'charles howell iii_12': 12, 'reset points_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'player_7': 'player', 'kj choi_8': 'kj choi', 'reset points_9': 'reset points', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'charles howell iii_12': 'charles howell iii', 'reset points_13': 'reset points'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'kj choi_8': [0], 'reset points_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'charles howell iii_12': [1], 'reset points_13': [3]} | ['', 'player', 'country', 'points', 'events', 'reset points'] | [['1', 'tiger woods', 'united states', '30574', '13', '100000'], ['2', 'vijay singh', 'fiji', '19129', '23', '99000'], ['3', 'jim furyk', 'united states', '16691', '19', '98500'], ['4', 'phil mickelson', 'united states', '16037', '18', '98000'], ['5', 'kj choi', 'south korea', '15485', '21', '97500'], ['6', 'rory sabbatini', 'south africa', '13548', '19', '97250'], ['7', 'zach johnson', 'united states', '13341', '19', '97000'], ['8', 'charles howell iii', 'united states', '12126', '21', '96750'], ['9', 'brandt snedeker', 'united states', '11870', '25', '96500']] |
q force | https://en.wikipedia.org/wiki/Q_Force | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12339816-1.html.csv | majority | most of the products have a serial number of at least 934000 . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '934000', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'serial number', '934000'], 'result': True, 'ind': 0, 'tointer': 'for the serial number records of all rows , most of them are greater than 934000 .', 'tostr': 'most_greater { all_rows ; serial number ; 934000 } = true'} | most_greater { all_rows ; serial number ; 934000 } = true | for the serial number records of all rows , most of them are greater than 934000 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'serial number_3': 3, '934000_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'serial number_3': 'serial number', '934000_4': '934000'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'serial number_3': [0], '934000_4': [0]} | ['code name', 'function ( figure )', 'real name', 'birthplace', 'serial number', 'primary military speciality', 'secondary military speciality', 'equipment'] | [['shark', 'aqua trooper', 'jean - paul rives', 'toulouse', 'af 934038', 'torpedo technology', 'underwater demolition', 'breathing apparatus'], ['leviathan', 'deep sea defender', 'jamie hugh maclaren', 'glasgow', 'af 93403', 'naval battle tactics', 'gunnery', 'a red aerial and a red backpack'], ['phones', 'sonar officer', "patrick liam o'flaherty", 'dublin', 'af 934037', 'communications', 'survivor', 'radio pack and ak - 47'], ['surfer', 'sea skimmer pilot', 'hoxworth whipple', 'hawaii', 'af 934119', 'seaborne rescue', 'rocket assault', 'jet - ski ( sea skimmer ) armed with rockets'], ['dolphin', 'pilot of sealion', 'gareth morgan', 'cardiff', 'af 934332', 'underwater solo attack', 'deep sea exploration', 'pilot accompanying sealion vehicle']] |
sport in saint petersburg | https://en.wikipedia.org/wiki/Sport_in_Saint_Petersburg | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12978801-1.html.csv | majority | the majority of all sports venues were established before 2005 . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '2005', 'subset': None} | {'func': 'most_less', 'args': ['all_rows', 'established', '2005'], 'result': True, 'ind': 0, 'tointer': 'for the established records of all rows , most of them are less than 2005 .', 'tostr': 'most_less { all_rows ; established ; 2005 } = true'} | most_less { all_rows ; established ; 2005 } = true | for the established records of all rows , most of them are less than 2005 . | 1 | 1 | {'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'established_3': 3, '2005_4': 4} | {'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'established_3': 'established', '2005_4': '2005'} | {'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'established_3': [0], '2005_4': [0]} | ['club', 'league', 'sport', 'venue', 'established'] | [['zenit st petersburg', 'rfpl', 'football', 'petrovsky stadium', '1926'], ['spartak st petersburg', 'pbl', 'basketball', 'yubileyny sports palace', '1935'], ['avtomobilist st petesburg', 'vsl', 'volleyball', 'platonov volleyball academy', '1935'], ['ska st petersburg', 'khl', 'ice hockey', 'ice palace', '1946'], ['politekh st petersburg', 'mfsl', 'futsal', 'kalinin district mfok', '1995'], ['petrotrest st petersburg', 'fnl', 'football', 'msa petrovsky', '2001'], ['ska - 1946 st petersburg', 'mhl', 'ice hockey', 'msa yubileyny', '2009'], ['serebryanye lvy', 'mhl', 'ice hockey', 'spartak ice palace', '2010']] |
list of government bonds | https://en.wikipedia.org/wiki/List_of_government_bonds | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2764267-2.html.csv | unique | japanese yen is the only government bond with a 157.5 percent financial liabilities value of gdp . | {'scope': 'all', 'row': '1', 'col': '6', 'col_other': '1,2', 'criterion': 'equal', 'value': '157.5 %', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'government financial liabilities as % of gdp ( end 2003 )', '157.5 %'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose government financial liabilities as % of gdp ( end 2003 ) record fuzzily matches to 157.5 % .', 'tostr': 'filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % } }', 'tointer': 'select the rows whose government financial liabilities as % of gdp ( end 2003 ) record fuzzily matches to 157.5 % . there is only one such row in the table .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'government financial liabilities as % of gdp ( end 2003 )', '157.5 %'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose government financial liabilities as % of gdp ( end 2003 ) record fuzzily matches to 157.5 % .', 'tostr': 'filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % }'}, 'currency'], 'result': 'yen', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % } ; currency }'}, 'yen'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % } ; currency } ; yen }', 'tointer': 'the currency record of this unqiue row is yen .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'government financial liabilities as % of gdp ( end 2003 )', '157.5 %'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose government financial liabilities as % of gdp ( end 2003 ) record fuzzily matches to 157.5 % .', 'tostr': 'filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % }'}, 'country'], 'result': 'japan', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % } ; country }'}, 'japan'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % } ; country } ; japan }', 'tointer': 'the country record of this unqiue row is japan .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % } ; currency } ; yen } ; eq { hop { filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % } ; country } ; japan } }', 'tointer': 'the currency record of this unqiue row is yen . the country record of this unqiue row is japan .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % } } ; and { eq { hop { filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % } ; currency } ; yen } ; eq { hop { filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % } ; country } ; japan } } } = true', 'tointer': 'select the rows whose government financial liabilities as % of gdp ( end 2003 ) record fuzzily matches to 157.5 % . there is only one such row in the table . the currency record of this unqiue row is yen . the country record of this unqiue row is japan .'} | and { only { filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % } } ; and { eq { hop { filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % } ; currency } ; yen } ; eq { hop { filter_eq { all_rows ; government financial liabilities as % of gdp ( end 2003 ) ; 157.5 % } ; country } ; japan } } } = true | select the rows whose government financial liabilities as % of gdp ( end 2003 ) record fuzzily matches to 157.5 % . there is only one such row in the table . the currency record of this unqiue row is yen . the country record of this unqiue row is japan . | 10 | 8 | {'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_9': 9, 'government financial liabilities as % of gdp (end 2003)_10': 10, '157.5%_11': 11, 'and_6': 6, 'str_eq_3': 3, 'str_hop_2': 2, 'currency_12': 12, 'yen_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'country_14': 14, 'japan_15': 15} | {'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_9': 'all_rows', 'government financial liabilities as % of gdp (end 2003)_10': 'government financial liabilities as % of gdp ( end 2003 )', '157.5%_11': '157.5 %', 'and_6': 'and', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'currency_12': 'currency', 'yen_13': 'yen', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'country_14': 'country', 'japan_15': 'japan'} | {'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_str_eq_0': [1, 2, 4], 'all_rows_9': [0], 'government financial liabilities as % of gdp (end 2003)_10': [0], '157.5%_11': [0], 'and_6': [7], 'str_eq_3': [6], 'str_hop_2': [3], 'currency_12': [2], 'yen_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'country_14': [4], 'japan_15': [5]} | ['currency', 'country', 'generic name or nickname', 'rating ( s & p / moodys )', 'negotiable debt at mid - 2005 ( us dollar bn equivalent )', 'government financial liabilities as % of gdp ( end 2003 )', 'issuer', 'internet site'] | [['yen', 'japan', 's jgb', 'aa - / a2', '6666', '157.5 %', 'ministry of finance ( mof )', 'site'], ['us dollar', 'united states', 'us treasuries', 'aa + / aaa', '4000', '62.5 %', 'bureau of the public debt', 'site'], ['euro', 'italy', 's btp', 'bbb + / baa2', '1530', '120.9 %', 'dipartimento del tesoro', 'site'], ['euro', 'france', 's oat', 'aa + / aaa', '1300', '71.2 %', 'agence france trãsor', 'site'], ['euro', 'germany', 'bunds', 'aaa / aaa', '1020', '65.1 %', 'finanzagentur gmbh', 'site']] |
fivb volleyball world championship | https://en.wikipedia.org/wiki/FIVB_Volleyball_World_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1747960-4.html.csv | majority | most of the teams in the fivb volleyball world championship did not win a gold medal . | {'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'equal', 'value': '0', 'subset': None} | {'func': 'most_eq', 'args': ['all_rows', 'gold', '0'], 'result': True, 'ind': 0, 'tointer': 'for the gold records of all rows , most of them are equal to 0 .', 'tostr': 'most_eq { all_rows ; gold ; 0 } = true'} | most_eq { all_rows ; gold ; 0 } = true | for the gold records of all rows , most of them are equal to 0 . | 1 | 1 | {'most_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'gold_3': 3, '0_4': 4} | {'most_eq_0': 'most_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'gold_3': 'gold', '0_4': '0'} | {'most_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'gold_3': [0], '0_4': [0]} | ['rank', 'gold', 'silver', 'bronze', 'total'] | [['1', '7', '2', '4', '13'], ['2', '3', '3', '1', '7'], ['3', '3', '1', '0', '4'], ['4', '2', '2', '0', '4'], ['5', '1', '0', '0', '1'], ['6', '0', '3', '0', '3'], ['7', '0', '2', '2', '4'], ['8', '0', '1', '2', '3'], ['9', '0', '1', '1', '2'], ['10', '0', '1', '0', '1'], ['11', '0', '0', '2', '2'], ['13', '0', '0', '1', '1']] |
george ker | https://en.wikipedia.org/wiki/George_Ker | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12738014-1.html.csv | count | 6 of george ker 's goals took place in glasgow . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'glasgow', 'result': '6', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'glasgow'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to glasgow .', 'tostr': 'filter_eq { all_rows ; venue ; glasgow }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; venue ; glasgow } }', 'tointer': 'select the rows whose venue record fuzzily matches to glasgow . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; venue ; glasgow } } ; 6 } = true', 'tointer': 'select the rows whose venue record fuzzily matches to glasgow . the number of such rows is 6 .'} | eq { count { filter_eq { all_rows ; venue ; glasgow } } ; 6 } = true | select the rows whose venue record fuzzily matches to glasgow . the number of such rows is 6 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'venue_5': 5, 'glasgow_6': 6, '6_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'venue_5': 'venue', 'glasgow_6': 'glasgow', '6_7': '6'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'venue_5': [0], 'glasgow_6': [0], '6_7': [2]} | ['date', 'venue', 'score', 'result', 'competition'] | [['13 march 1880', 'hampden park , glasgow', '1 - 0', '5 - 4', 'friendly'], ['13 march 1880', 'hampden park , glasgow', '3 - 2', '5 - 4', 'friendly'], ['13 march 1880', 'hampden park , glasgow', '4 - 2', '5 - 4', 'friendly'], ['12 march 1881', 'kennington oval , london', '4 - 1', '6 - 1', 'friendly'], ['12 march 1881', 'kennington oval , london', '6 - 1', '6 - 1', 'friendly'], ['14 march 1881', 'acton park , wrexham', '1 - 1', '5 - 1', 'friendly'], ['14 march 1881', 'acton park , wrexham', '4 - 1', '5 - 1', 'friendly'], ['11 march 1882', 'hampden park , glasgow', '2 - 1', '5 - 1', 'friendly'], ['11 march 1882', 'hampden park , glasgow', '5 - 1', '5 - 1', 'friendly'], ['25 march 1882', 'hampden park , glasgow', '2 - 0', '5 - 0', 'friendly']] |
list of dual - code rugby internationals | https://en.wikipedia.org/wiki/List_of_dual-code_rugby_internationals | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18860278-11.html.csv | count | two of the players in the list of dual - code rugby internationals made their rugby union debut against france . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'france', 'result': '2', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', "int ' l debut", 'france'], 'result': None, 'ind': 0, 'tointer': "select the rows whose int ' l debut record fuzzily matches to france .", 'tostr': "filter_eq { all_rows ; int ' l debut ; france }"}], 'result': '2', 'ind': 1, 'tostr': "count { filter_eq { all_rows ; int ' l debut ; france } }", 'tointer': "select the rows whose int ' l debut record fuzzily matches to france . the number of such rows is 2 ."}, '2'], 'result': True, 'ind': 2, 'tostr': "eq { count { filter_eq { all_rows ; int ' l debut ; france } } ; 2 } = true", 'tointer': "select the rows whose int ' l debut record fuzzily matches to france . the number of such rows is 2 ."} | eq { count { filter_eq { all_rows ; int ' l debut ; france } } ; 2 } = true | select the rows whose int ' l debut record fuzzily matches to france . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, "int'l debut_5": 5, 'france_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', "int'l debut_5": "int ' l debut", 'france_6': 'france', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], "int'l debut_5": [0], 'france_6': [0], '2_7': [2]} | ['player', "int ' l debut", 'year', 'cross code debut', 'date', 'position'] | [['alex laidlaw', 'ru test v ireland', '1897', 'rl test other nationalities v england', '1905 or 1906', 'forward'], ['roy muir kinnear', 'british lions v south africa', '1924', 'rl 1st test great britain v australia', '5 oct 1929', 'centre'], ['dave valentine', 'ru five nations v ireland', '1947', 'rl 1st test great britain v australia', '9 oct 1948', 'forward'], ['david rose', 'ru test v france', '1951', 'rlwc great britain v australia', '13 nov 1954', 'three - quarter'], ['alan tait', 'rwc v france', '1987', 'rlwc great britain v australia', '24 oct 1992', 'back'], ['andy craig', 'rl test v wales', '1999', 'ru test v canada', '15 june 2002', 'centre']] |
1998 atp super 9 | https://en.wikipedia.org/wiki/1998_ATP_Super_9 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16381982-1.html.csv | count | four of the tournaments under super 9 were played on hard surface . | {'scope': 'all', 'criterion': 'equal', 'value': 'hard', 'result': '4', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'surface', 'hard'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose surface record fuzzily matches to hard .', 'tostr': 'filter_eq { all_rows ; surface ; hard }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; surface ; hard } }', 'tointer': 'select the rows whose surface record fuzzily matches to hard . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; surface ; hard } } ; 4 } = true', 'tointer': 'select the rows whose surface record fuzzily matches to hard . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; surface ; hard } } ; 4 } = true | select the rows whose surface record fuzzily matches to hard . the number of such rows is 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'surface_5': 5, 'hard_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'surface_5': 'surface', 'hard_6': 'hard', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'surface_5': [0], 'hard_6': [0], '4_7': [2]} | ['tournament', 'surface', 'week', 'winner and score', 'finalist', 'semifinalists'] | [['indian wells', 'hard', 'march 9', 'marcelo ríos 6 - 3 , 6 - 7 ( 15 ) , 7 - 6 ( 4 ) , 6 - 4', 'greg rusedski', 'thomas muster jan - michael gambill'], ['key biscane', 'hard', 'march 16', 'marcelo ríos 7 - 5 , 6 - 3 , 6 - 4', 'andre agassi', 'àlex corretja tim henman'], ['monte carlo', 'clay', 'april 20', 'carlos moyá 6 - 3 , 6 - 0 , 7 - 5', 'cédric pioline', 'alberto berasategui richard krajicek'], ['hamburg', 'clay', 'may 4', 'albert costa 6 - 2 , 6 - 0 , 1 - 0 ret', 'àlex corretja', 'karol kučera félix mantilla'], ['rome', 'clay', 'may 11', 'marcelo ríos w / o', 'albert costa', 'alberto berasategui gustavo kuerten'], ['toronto', 'hard', 'august 3', 'patrick rafter 7 - 6 ( 3 ) , 6 - 4', 'richard krajicek', 'andre agassi tim henman'], ['cincinnati', 'hard', 'august 10', 'patrick rafter 1 - 6 , 7 - 6 ( 2 ) , 6 - 4', 'pete sampras', 'magnus larsson yevgeny kafelnikov'], ['stuttgart', 'carpet ( i )', 'october 26', 'richard krajicek 6 - 4 , 6 - 3 , 6 - 3', 'yevgeny kafelnikov', 'pete sampras jonas björkman'], ['paris', 'carpet ( i )', 'november 2', 'greg rusedski 6 - 4 , 7 - 6 ( 4 ) , 6 - 3', 'pete sampras', 'todd martin yevgeny kafelnikov']] |
northern state conference ( ihsaa ) | https://en.wikipedia.org/wiki/Northern_State_Conference_%28IHSAA%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18936749-1.html.csv | comparative | of the northern state conference ( ihsaa ) members , knox community has a larger enrollment than culver community . | {'row_1': '5', 'row_2': '2', 'col': '4', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'school ( ihsaa id )', 'knox community'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose school ( ihsaa id ) record fuzzily matches to knox community .', 'tostr': 'filter_eq { all_rows ; school ( ihsaa id ) ; knox community }'}, 'enrollment'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; school ( ihsaa id ) ; knox community } ; enrollment }', 'tointer': 'select the rows whose school ( ihsaa id ) record fuzzily matches to knox community . take the enrollment record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'school ( ihsaa id )', 'culver community'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose school ( ihsaa id ) record fuzzily matches to culver community .', 'tostr': 'filter_eq { all_rows ; school ( ihsaa id ) ; culver community }'}, 'enrollment'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; school ( ihsaa id ) ; culver community } ; enrollment }', 'tointer': 'select the rows whose school ( ihsaa id ) record fuzzily matches to culver community . take the enrollment record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; school ( ihsaa id ) ; knox community } ; enrollment } ; hop { filter_eq { all_rows ; school ( ihsaa id ) ; culver community } ; enrollment } } = true', 'tointer': 'select the rows whose school ( ihsaa id ) record fuzzily matches to knox community . take the enrollment record of this row . select the rows whose school ( ihsaa id ) record fuzzily matches to culver community . take the enrollment record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; school ( ihsaa id ) ; knox community } ; enrollment } ; hop { filter_eq { all_rows ; school ( ihsaa id ) ; culver community } ; enrollment } } = true | select the rows whose school ( ihsaa id ) record fuzzily matches to knox community . take the enrollment record of this row . select the rows whose school ( ihsaa id ) record fuzzily matches to culver community . take the enrollment record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'school (ihsaa id)_7': 7, 'knox community_8': 8, 'enrollment_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'school (ihsaa id)_11': 11, 'culver community_12': 12, 'enrollment_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'school (ihsaa id)_7': 'school ( ihsaa id )', 'knox community_8': 'knox community', 'enrollment_9': 'enrollment', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'school (ihsaa id)_11': 'school ( ihsaa id )', 'culver community_12': 'culver community', 'enrollment_13': 'enrollment'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'school (ihsaa id)_7': [0], 'knox community_8': [0], 'enrollment_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'school (ihsaa id)_11': [1], 'culver community_12': [1], 'enrollment_13': [3]} | ['school ( ihsaa id )', 'location', 'mascot', 'enrollment', 'ihsaa class', 'county', 'year joined'] | [['bremen', 'bremen', 'lions', '505', 'aa', '50 marshall', '1989'], ['culver community', 'culver', 'cavaliers', '306', 'a', '50 marshall', '1977'], ['glenn', 'walkerton', 'falcons', '613', 'aaa', '71 st joseph', '1966'], ['jimtown', 'elkhart', 'jimmies', '642', 'aaa', '20 elkhart', '1966'], ['knox community', 'knox', 'redskins', '632', 'aaa', '75 starke', '1982'], ['laville', 'lakeville', 'lancers', '413', 'a', '71 st joseph', '1966'], ['new prairie', 'new carlisle', 'cougars', '859', 'aaaa', '46 laporte 71 st joseph', '1968'], ['triton', 'bourbon', 'trojans', '333', 'a', '50 marshall', '1980']] |
lehigh valley | https://en.wikipedia.org/wiki/Lehigh_Valley | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1165886-2.html.csv | superlative | the highest number of championships was won in lehigh valley in 1979 . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '6', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'championships'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; championships }'}, 'established'], 'result': '1979', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; championships } ; established }'}, '1979'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; championships } ; established } ; 1979 } = true', 'tointer': 'select the row whose championships record of all rows is maximum . the established record of this row is 1979 .'} | eq { hop { argmax { all_rows ; championships } ; established } ; 1979 } = true | select the row whose championships record of all rows is maximum . the established record of this row is 1979 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'championships_5': 5, 'established_6': 6, '1979_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'championships_5': 'championships', 'established_6': 'established', '1979_7': '1979'} | {'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'championships_5': [0], 'established_6': [1], '1979_7': [2]} | ['club', 'league', 'sport', 'venue', 'established', 'championships'] | [['lehigh valley storm', 'bneff', 'football', 'j birney crum stadium', '2010', '0'], ['lehigh valley ironpigs', 'il', 'baseball', 'coca - cola park', '2008', '0'], ['lehigh valley steelhawks', 'ifl', 'indoor football', 'stabler arena', '2011', '0'], ['fc sonic lehigh valley', 'npsl', 'soccer', "lehigh university 's ulrich sports complex", '2009', '0'], ['northampton laurels fc', 'wpsl', 'soccer', 'j birney crum stadium', '2005', '0'], ['pennsylvania stoners', 'npsl', 'soccer', 'j birney crum stadium', '1979', '1 ( 1980 )'], ['lehigh valley cricket club', 'pcl', 'cricket', 'lehigh valley velodrome', '1995', '0']] |
kristína kučová | https://en.wikipedia.org/wiki/Krist%C3%ADna_Ku%C4%8Dov%C3%A1 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14359057-4.html.csv | majority | most of the games kristína kučová played in the doubles were played on a clay surface . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'clay', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'surface', 'clay'], 'result': True, 'ind': 0, 'tointer': 'for the surface records of all rows , most of them fuzzily match to clay .', 'tostr': 'most_eq { all_rows ; surface ; clay } = true'} | most_eq { all_rows ; surface ; clay } = true | for the surface records of all rows , most of them fuzzily match to clay . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'surface_3': 3, 'clay_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'surface_3': 'surface', 'clay_4': 'clay'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'surface_3': [0], 'clay_4': [0]} | ['outcome', 'date', 'tournament', 'surface', 'partner', 'opponents in the final', 'score'] | [['winner', '17 march 2007', 'cairo', 'clay', 'zuzana kučová', 'melissa berry michelle gerards', '6 - 7 ( 3 ) 6 - 4 6 - 3'], ['winner', '20 may 2007', 'michalovce', 'clay', 'klaudia boczová', 'olga brózda justyna jegiołka', '7 - 5 4 - 6 6 - 3'], ['runner - up', '11 may 2008', 'jounieh', 'clay', 'stefanie vögele', 'nina bratchikova veronika kapshay', '5 - 7 , 6 - 3 ,'], ['winner', '25 may 2008', 'galați', 'clay', 'valentina sulpizio', 'alexandra cadanțu antonia xenia tout', '6 - 0 6 - 2'], ['runner - up', '3 may 2009', 'johannesburg', 'hard', 'anastasija sevastova', 'naomi cavaday lesia tsurenko', '2 - 6 6 - 2'], ['winner', '14 june 2009', 'zlín', 'clay', 'zuzana kučová', 'nikola fraňková carmen klaschka', '6 - 3 6 - 4']] |
grid energy storage | https://en.wikipedia.org/wiki/Grid_energy_storage | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1646838-1.html.csv | majority | the majority of grid energy storage technologies do not use any rare metals . | {'scope': 'all', 'col': '7', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'no', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'rare metals', 'no'], 'result': True, 'ind': 0, 'tointer': 'for the rare metals records of all rows , most of them fuzzily match to no .', 'tostr': 'most_eq { all_rows ; rare metals ; no } = true'} | most_eq { all_rows ; rare metals ; no } = true | for the rare metals records of all rows , most of them fuzzily match to no . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'rare metals_3': 3, 'no_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'rare metals_3': 'rare metals', 'no_4': 'no'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'rare metals_3': [0], 'no_4': [0]} | ['technology', 'moving parts', 'room temperature', 'flammable', 'toxic materials', 'in production', 'rare metals'] | [['flow', 'yes', 'yes', 'no', 'yes', 'no', 'no'], ['liquid metal', 'no', 'no', 'yes', 'no', 'no', 'no'], ['sodium - ion', 'no', 'no', 'yes', 'no', 'no', 'no'], ['lead - acid', 'no', 'yes', 'no', 'yes', 'yes', 'no'], ['sodium - sulfur batteries', 'no', 'no', 'no', 'yes', 'yes', 'no'], ['ni - cd', 'no', 'yes', 'no', 'yes', 'yes', 'yes'], ['lithium - ion', 'no', 'yes', 'yes', 'no', 'yes', 'no']] |
2006 - 07 coventry city f.c. season | https://en.wikipedia.org/wiki/2006%E2%80%9307_Coventry_City_F.C._season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12808457-2.html.csv | ordinal | robert page had the 2nd highest number of championship participation in the 2006 - 07 coventry city f.c. season . | {'row': '2', 'col': '2', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'championship', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; championship ; 2 }'}, 'name'], 'result': 'robert page', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; championship ; 2 } ; name }'}, 'robert page'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; championship ; 2 } ; name } ; robert page } = true', 'tointer': 'select the row whose championship record of all rows is 2nd maximum . the name record of this row is robert page .'} | eq { hop { nth_argmax { all_rows ; championship ; 2 } ; name } ; robert page } = true | select the row whose championship record of all rows is 2nd maximum . the name record of this row is robert page . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'championship_5': 5, '2_6': 6, 'name_7': 7, 'robert page_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'championship_5': 'championship', '2_6': '2', 'name_7': 'name', 'robert page_8': 'robert page'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'championship_5': [0], '2_6': [0], 'name_7': [1], 'robert page_8': [2]} | ['name', 'championship', 'league cup', 'fa cup', 'total'] | [['kevin kyle', '11', '0', '1', '12'], ['robert page', '10', '0', '0', '10'], ['michael doyle', '8', '0', '2', '10'], ['andrew whing', '6', '1', '0', '7'], ['david mcnamee', '6', '0', '0', '6'], ['marcus hall', '5', '0', '0', '5'], ['leon mckenzie', '5', '0', '0', '5'], ['jay tabb', '5', '0', '0', '5'], ['elliott ward', '5', '0', '0', '5'], ['richard duffy', '4', '0', '0', '4'], ['stephen hughes', '3', '1', '0', '3'], ['dele adebola', '3', '0', '0', '3'], ['isaac osbourne', '3', '0', '0', '3'], ['kevin thornton', '3', '0', '0', '3'], ['adam virgo', '2', '0', '1', '3'], ['colin cameron', '1', '0', '0', '1'], ['colin hawkins', '1', '0', '0', '1'], ['stern john', '1', '0', '0', '1']] |
Subsets and Splits