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franca fiacconi | https://en.wikipedia.org/wiki/Franca_Fiacconi | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10682522-1.html.csv | aggregation | franca fiacconi 's total competition time from 1993 through 2001 was 22:36:29 . | {'scope': 'all', 'col': '5', 'type': 'sum', 'result': '22:36:29', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'notes'], 'result': '22:36:29', 'ind': 0, 'tostr': 'sum { all_rows ; notes }'}, '22:36:29'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; notes } ; 22:36:29 } = true', 'tointer': 'the sum of the notes record of all rows is 22:36:29 .'} | round_eq { sum { all_rows ; notes } ; 22:36:29 } = true | the sum of the notes record of all rows is 22:36:29 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'notes_4': 4, '22:36:29_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'notes_4': 'notes', '22:36:29_5': '22:36:29'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'notes_4': [0], '22:36:29_5': [1]} | ['year', 'competition', 'venue', 'position', 'notes'] | [['1993', 'world student games', 'buffalo , united states', '2nd', '2:38:44'], ['1996', 'italian marathon', 'carpi , italy', '1st', '2:28:22'], ['1997', 'world championships', 'athens , greece', '13th', '2:39:53'], ['1998', 'rome city marathon', 'rome , italy', '1st', '2:28:12'], ['1998', 'european championships', 'budapest , hungary', '4th', '2:28:59'], ['1998', 'new york city marathon', 'new york , united states', '1st', '2:25:17'], ['1999', 'prague marathon', 'prague , czech republic', '1st', '2:28:33'], ['2001', 'osaka international ladies marathon', 'osaka , japan', '2nd', '2:26:49'], ['2001', 'enschede marathon', 'enschede , netherlands', '1st', '2:31:40']] |
washington redskins draft history | https://en.wikipedia.org/wiki/Washington_Redskins_draft_history | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17100961-73.html.csv | unique | in the washington redskins draft history , the only player to go to college at tulane was patrick ramsey . | {'scope': 'all', 'row': '1', 'col': '6', 'col_other': '4', 'criterion': 'equal', 'value': 'tulane', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'tulane'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to tulane .', 'tostr': 'filter_eq { all_rows ; college ; tulane }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; college ; tulane } }', 'tointer': 'select the rows whose college record fuzzily matches to tulane . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'tulane'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to tulane .', 'tostr': 'filter_eq { all_rows ; college ; tulane }'}, 'name'], 'result': 'patrick ramsey', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; college ; tulane } ; name }'}, 'patrick ramsey'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; college ; tulane } ; name } ; patrick ramsey }', 'tointer': 'the name record of this unqiue row is patrick ramsey .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; college ; tulane } } ; eq { hop { filter_eq { all_rows ; college ; tulane } ; name } ; patrick ramsey } } = true', 'tointer': 'select the rows whose college record fuzzily matches to tulane . there is only one such row in the table . the name record of this unqiue row is patrick ramsey .'} | and { only { filter_eq { all_rows ; college ; tulane } } ; eq { hop { filter_eq { all_rows ; college ; tulane } ; name } ; patrick ramsey } } = true | select the rows whose college record fuzzily matches to tulane . there is only one such row in the table . the name record of this unqiue row is patrick ramsey . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'college_7': 7, 'tulane_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'patrick ramsey_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'college_7': 'college', 'tulane_8': 'tulane', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'patrick ramsey_10': 'patrick ramsey'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'college_7': [0], 'tulane_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'patrick ramsey_10': [3]} | ['round', 'pick', 'overall', 'name', 'position', 'college'] | [['1', '32', '32', 'patrick ramsey', 'qb', 'tulane'], ['2', '24', '56', 'ladell betts', 'rb', 'iowa'], ['3', '14', '79', 'rashad bauman', 'cb', 'oregon'], ['3', '22', '87', 'cliff russell', 'wr', 'utah'], ['5', '24', '159', 'andre lott', 's', 'tennessee'], ['5', '25', '160', 'robert royal', 'te', 'louisiana state'], ['6', '20', '192', 'reggie coleman', 'ot', 'tennessee'], ['7', '19', '230', 'jeff grau', 'ls', 'ucla'], ['7', '23', '234', 'greg scott', 'de', 'hampton'], ['7', '46', '257', 'rock cartwright', 'fb', 'kansas state']] |
maine locations by per capita income | https://en.wikipedia.org/wiki/Maine_locations_by_per_capita_income | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1421760-1.html.csv | ordinal | piscataquis is the location of maine that has the second lowest per capita income . | {'row': '17', 'col': '2', 'order': '2', 'col_other': '1', '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', 'per capita income', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; per capita income ; 2 }'}, 'county'], 'result': 'piscataquis', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; per capita income ; 2 } ; county }'}, 'piscataquis'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; per capita income ; 2 } ; county } ; piscataquis } = true', 'tointer': 'select the row whose per capita income record of all rows is 2nd minimum . the county record of this row is piscataquis .'} | eq { hop { nth_argmin { all_rows ; per capita income ; 2 } ; county } ; piscataquis } = true | select the row whose per capita income record of all rows is 2nd minimum . the county record of this row is piscataquis . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'per capita income_5': 5, '2_6': 6, 'county_7': 7, 'piscataquis_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', 'per capita income_5': 'per capita income', '2_6': '2', 'county_7': 'county', 'piscataquis_8': 'piscataquis'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'per capita income_5': [0], '2_6': [0], 'county_7': [1], 'piscataquis_8': [2]} | ['county', 'per capita income', 'median household income', 'median family income', 'population', 'number of households'] | [['cumberland', '31041', '55658', '71335', '281674', '117339'], ['lincoln', '28003', '47678', '58028', '34457', '15149'], ['united states', '27334', '51914', '62982', '308745538', '116716292'], ['york', '27137', '55008', '65077', '197131', '81009'], ['sagadahoc', '26983', '55486', '66650', '35293', '15088'], ['hancock', '26876', '47533', '60092', '54418', '24221'], ['maine', '25385', '46933', '58185', '1328361', '557219'], ['knox', '25291', '45264', '55830', '39736', '17258'], ['kennebec', '24656', '45973', '56853', '122151', '51128'], ['penobscot', '22977', '42658', '54271', '153923', '62966'], ['androscoggin', '22752', '44470', '55045', '107702', '44315'], ['waldo', '22213', '41312', '50222', '38786', '16431'], ['oxford', '21254', '39748', '48000', '57833', '24300'], ['franklin', '20838', '39831', '48634', '30768', '13000'], ['somerset', '20709', '36647', '47177', '52228', '21927'], ['aroostook', '20251', '36574', '47114', '71870', '30961'], ['piscataquis', '19870', '34016', '43821', '17535', '7825'], ['washington', '19401', '34859', '43612', '32856', '14302']] |
administrative division of congress poland | https://en.wikipedia.org/wiki/Administrative_division_of_Congress_Poland | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11614581-3.html.csv | comparative | piotrków governorate had a higher population compared to the płock governorate , regarding the administrative regions of poland congress from 1893 to 1912 . | {'row_1': '6', 'row_2': '7', 'col': '6', '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', 'governorate', 'piotrków governorate'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose governorate record fuzzily matches to piotrków governorate .', 'tostr': 'filter_eq { all_rows ; governorate ; piotrków governorate }'}, 'population , in thousands , ( 1905 )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; governorate ; piotrków governorate } ; population , in thousands , ( 1905 ) }', 'tointer': 'select the rows whose governorate record fuzzily matches to piotrków governorate . take the population , in thousands , ( 1905 ) record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'governorate', 'płock governorate'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose governorate record fuzzily matches to płock governorate .', 'tostr': 'filter_eq { all_rows ; governorate ; płock governorate }'}, 'population , in thousands , ( 1905 )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; governorate ; płock governorate } ; population , in thousands , ( 1905 ) }', 'tointer': 'select the rows whose governorate record fuzzily matches to płock governorate . take the population , in thousands , ( 1905 ) record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; governorate ; piotrków governorate } ; population , in thousands , ( 1905 ) } ; hop { filter_eq { all_rows ; governorate ; płock governorate } ; population , in thousands , ( 1905 ) } } = true', 'tointer': 'select the rows whose governorate record fuzzily matches to piotrków governorate . take the population , in thousands , ( 1905 ) record of this row . select the rows whose governorate record fuzzily matches to płock governorate . take the population , in thousands , ( 1905 ) record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; governorate ; piotrków governorate } ; population , in thousands , ( 1905 ) } ; hop { filter_eq { all_rows ; governorate ; płock governorate } ; population , in thousands , ( 1905 ) } } = true | select the rows whose governorate record fuzzily matches to piotrków governorate . take the population , in thousands , ( 1905 ) record of this row . select the rows whose governorate record fuzzily matches to płock governorate . take the population , in thousands , ( 1905 ) 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, 'governorate_7': 7, 'piotrków governorate_8': 8, 'population , in thousands , ( 1905 )_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'governorate_11': 11, 'płock governorate_12': 12, 'population , in thousands , ( 1905 )_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', 'governorate_7': 'governorate', 'piotrków governorate_8': 'piotrków governorate', 'population , in thousands , ( 1905 )_9': 'population , in thousands , ( 1905 )', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'governorate_11': 'governorate', 'płock governorate_12': 'płock governorate', 'population , in thousands , ( 1905 )_13': 'population , in thousands , ( 1905 )'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'governorate_7': [0], 'piotrków governorate_8': [0], 'population , in thousands , ( 1905 )_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'governorate_11': [1], 'płock governorate_12': [1], 'population , in thousands , ( 1905 )_13': [3]} | ['governorate', 'name in russian', 'name in polish', 'seat', 'area , in thousands of km 2', 'population , in thousands , ( 1905 )'] | [['warsaw governorate', 'варшавская губерния', 'gubernia warszawska', 'warszawa', '176', '2233'], ['kalisz governorate', 'калишская губерния', 'gubernia kaliska', 'kalisz', '113', '964'], ['kielce governorate', 'келецкая губерния', 'gubernia kielecka', 'kielce', '102', '899'], ['łomża governorate', 'ломжинская губерния', 'gubernia lubelska', 'łomża', '106', '645'], ['lublin governorate', 'люблинская губерния', 'gubernia łomżyńska', 'lublin', '169', '1341'], ['piotrków governorate', 'петроковская губерния', 'gubernia piotrkowska', 'piotrków', '122', '1640'], ['płock governorate', 'плоцкая губерния', 'gubernia płocka', 'płock', '94', '613'], ['radom governorate', 'радомская губерния', 'gubernia radomska', 'radom', '124', '917'], ['siedlce governorate', 'седлецкая губерния', 'gubernia siedlecka', 'siedlce', '143', '894']] |
list of tallest buildings in slovenia | https://en.wikipedia.org/wiki/List_of_tallest_buildings_in_Slovenia | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15062421-2.html.csv | superlative | the dravska vrata building in maribor is the tallest building in slovenia . | {'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', 'height feet / metres'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; height feet / metres }'}, 'name'], 'result': 'dravska vrata', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; height feet / metres } ; name }'}, 'dravska vrata'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; height feet / metres } ; name } ; dravska vrata } = true', 'tointer': 'select the row whose height feet / metres record of all rows is maximum . the name record of this row is dravska vrata .'} | eq { hop { argmax { all_rows ; height feet / metres } ; name } ; dravska vrata } = true | select the row whose height feet / metres record of all rows is maximum . the name record of this row is dravska vrata . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'height feet / metres_5': 5, 'name_6': 6, 'dravska vrata_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'height feet / metres_5': 'height feet / metres', 'name_6': 'name', 'dravska vrata_7': 'dravska vrata'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'height feet / metres_5': [0], 'name_6': [1], 'dravska vrata_7': [2]} | ['rank', 'name', 'location', 'height feet / metres', 'floors', 'year proposed'] | [['1', 'dravska vrata', 'maribor', '112 m', 'n / a', 'n / a'], ['2', 'emonika', 'ljubljana', '107 m', 'n / a', '27'], ['3', 'kolizej centre', 'ljubljana', '75 m', '18', '2010'], ['4', 'severna mestna vrata', 'ljubljana', '2 x 72 m', '22', '2010'], ['5', 'hotel plaza , ljubljana', 'ljubljana', '60 m', '15', '2010']] |
1996 - 97 european challenge cup | https://en.wikipedia.org/wiki/1996%E2%80%9397_European_Challenge_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16770037-5.html.csv | comparative | borgoin had more tries for than london irish in the 1996-97 european challenge cup . | {'row_1': '1', 'row_2': '6', 'col': '2', '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', 'team', 'bourgoin'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team record fuzzily matches to bourgoin .', 'tostr': 'filter_eq { all_rows ; team ; bourgoin }'}, 'tries for'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; team ; bourgoin } ; tries for }', 'tointer': 'select the rows whose team record fuzzily matches to bourgoin . take the tries for record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'london irish'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose team record fuzzily matches to london irish .', 'tostr': 'filter_eq { all_rows ; team ; london irish }'}, 'tries for'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; team ; london irish } ; tries for }', 'tointer': 'select the rows whose team record fuzzily matches to london irish . take the tries for record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; team ; bourgoin } ; tries for } ; hop { filter_eq { all_rows ; team ; london irish } ; tries for } } = true', 'tointer': 'select the rows whose team record fuzzily matches to bourgoin . take the tries for record of this row . select the rows whose team record fuzzily matches to london irish . take the tries for record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; team ; bourgoin } ; tries for } ; hop { filter_eq { all_rows ; team ; london irish } ; tries for } } = true | select the rows whose team record fuzzily matches to bourgoin . take the tries for record of this row . select the rows whose team record fuzzily matches to london irish . take the tries for 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, 'team_7': 7, 'bourgoin_8': 8, 'tries for_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'team_11': 11, 'london irish_12': 12, 'tries for_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', 'team_7': 'team', 'bourgoin_8': 'bourgoin', 'tries for_9': 'tries for', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'team_11': 'team', 'london irish_12': 'london irish', 'tries for_13': 'tries for'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'team_7': [0], 'bourgoin_8': [0], 'tries for_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'team_11': [1], 'london irish_12': [1], 'tries for_13': [3]} | ['team', 'tries for', 'tries against', 'try diff', 'points for', 'points against', 'points diff'] | [['bourgoin', '27', '4', '+ 23', '196', '66', '+ 130'], ['bordeaux - bègles', '29', '13', '+ 16', '195', '99', '+ 96'], ['swansea', '28', '19', '+ 9', '207', '138', '+ 69'], ['gloucester', '17', '17', '0', '119', '123', '4'], ['ebbw vale', '6', '36', '30', '48', '243', '195'], ['london irish', '12', '30', '18', '90', '186', '96']] |
élie bayol | https://en.wikipedia.org/wiki/%C3%89lie_Bayol | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1228323-1.html.csv | comparative | elie bayol did better in 1954 than he did when competing in 1952 . | {'row_1': '4', 'row_2': '1', 'col': '5', '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', 'year', '1954'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record fuzzily matches to 1954 .', 'tostr': 'filter_eq { all_rows ; year ; 1954 }'}, 'points'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; year ; 1954 } ; points }', 'tointer': 'select the rows whose year record fuzzily matches to 1954 . take the points record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1952'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year record fuzzily matches to 1952 .', 'tostr': 'filter_eq { all_rows ; year ; 1952 }'}, 'points'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; year ; 1952 } ; points }', 'tointer': 'select the rows whose year record fuzzily matches to 1952 . take the points record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; year ; 1954 } ; points } ; hop { filter_eq { all_rows ; year ; 1952 } ; points } } = true', 'tointer': 'select the rows whose year record fuzzily matches to 1954 . take the points record of this row . select the rows whose year record fuzzily matches to 1952 . take the points record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; year ; 1954 } ; points } ; hop { filter_eq { all_rows ; year ; 1952 } ; points } } = true | select the rows whose year record fuzzily matches to 1954 . take the points record of this row . select the rows whose year record fuzzily matches to 1952 . take the 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, 'year_7': 7, '1954_8': 8, 'points_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'year_11': 11, '1952_12': 12, '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', 'year_7': 'year', '1954_8': '1954', 'points_9': 'points', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'year_11': 'year', '1952_12': '1952', 'points_13': 'points'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'year_7': [0], '1954_8': [0], 'points_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'year_11': [1], '1952_12': [1], 'points_13': [3]} | ['year', 'entrant', 'chassis', 'engine', 'points'] | [['1952', 'élie bayol', 'osca 20', 'osca straight - 6', '0'], ['1953', 'élie bayol', 'osca 20', 'osca straight - 6', '0'], ['1953', 'osca', 'osca 20', 'osca straight - 6', '0'], ['1954', 'equipe gordini', 'gordini type 16', 'gordini straight - 6', '2'], ['1955', 'equipe gordini', 'gordini type 16', 'gordini straight - 6', '0'], ['1956', 'gordini', 'gordini type 32', 'gordini straight - 8', '0']] |
2007 - 08 commonwealth bank series statistics | https://en.wikipedia.org/wiki/2007%E2%80%9308_Commonwealth_Bank_Series_statistics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15700367-3.html.csv | unique | adam gilchrist is the only player who managed to score 313 runs . | {'scope': 'all', 'row': '1', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': '313', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'runs scored', '313'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose runs scored record is equal to 313 .', 'tostr': 'filter_eq { all_rows ; runs scored ; 313 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; runs scored ; 313 } }', 'tointer': 'select the rows whose runs scored record is equal to 313 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'runs scored', '313'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose runs scored record is equal to 313 .', 'tostr': 'filter_eq { all_rows ; runs scored ; 313 }'}, 'name'], 'result': 'adam gilchrist ( wk )', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; runs scored ; 313 } ; name }'}, 'adam gilchrist ( wk )'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; runs scored ; 313 } ; name } ; adam gilchrist ( wk ) }', 'tointer': 'the name record of this unqiue row is adam gilchrist ( wk ) .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; runs scored ; 313 } } ; eq { hop { filter_eq { all_rows ; runs scored ; 313 } ; name } ; adam gilchrist ( wk ) } } = true', 'tointer': 'select the rows whose runs scored record is equal to 313 . there is only one such row in the table . the name record of this unqiue row is adam gilchrist ( wk ) .'} | and { only { filter_eq { all_rows ; runs scored ; 313 } } ; eq { hop { filter_eq { all_rows ; runs scored ; 313 } ; name } ; adam gilchrist ( wk ) } } = true | select the rows whose runs scored record is equal to 313 . there is only one such row in the table . the name record of this unqiue row is adam gilchrist ( wk ) . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'runs scored_7': 7, '313_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'adam gilchrist (wk)_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'runs scored_7': 'runs scored', '313_8': '313', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'adam gilchrist (wk)_10': 'adam gilchrist ( wk )'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'runs scored_7': [0], '313_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'adam gilchrist (wk)_10': [3]} | ['name', 'innings', 'runs scored', 'balls faced', 'average', 'sr'] | [['adam gilchrist ( wk )', '8', '313', '318', '39.13', '98.43'], ['matthew hayden', '6', '161', '231', '26.83', '69.70'], ['ricky ponting ( c )', '8', '189', '256', '23.63', '73.83'], ['michael clarke', '7', '293', '416', '48.83', '70.43'], ['andrew symonds', '8', '100', '125', '14.29', '80.00'], ['michael hussey', '7', '189', '283', '47.25', '66.78'], ['james hopes', '7', '115', '125', '16.43', '92.00'], ['brett lee', '5', '49', '102', '12.25', '48.04'], ['mitchell johnson', '5', '21', '44', '7.00', '47.73'], ['nathan bracken', '4', '16', '43', '5.33', '37.21'], ['stuart clark', '2', '8', '10', '8.00', '80.00'], ['brad haddin', '2', '12', '44', '6.00', '27.27'], ['brad hogg', '4', '62', '100', '15.50', '62.00']] |
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 | majority | most who do you think you are ? episodes originally aired in ' 07 . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': '2007', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'original air date', '2007'], 'result': True, 'ind': 0, 'tointer': 'for the original air date records of all rows , most of them fuzzily match to 2007 .', 'tostr': 'most_eq { all_rows ; original air date ; 2007 } = true'} | most_eq { all_rows ; original air date ; 2007 } = true | for the original air date records of all rows , most of them fuzzily match to 2007 . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'original air date_3': 3, '2007_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'original air date_3': 'original air date', '2007_4': '2007'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'original air date_3': [0], '2007_4': [0]} | ['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']] |
metro conference | https://en.wikipedia.org/wiki/Metro_Conference | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2293402-2.html.csv | superlative | the universe of cincinnati has the highest enrollment of all institutions . | {'scope': 'all', 'col_superlative': '6', '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', 'enrollment'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; enrollment }'}, 'institution'], 'result': 'university of cincinnati', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; enrollment } ; institution }'}, 'university of cincinnati'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; enrollment } ; institution } ; university of cincinnati } = true', 'tointer': 'select the row whose enrollment record of all rows is maximum . the institution record of this row is university of cincinnati .'} | eq { hop { argmax { all_rows ; enrollment } ; institution } ; university of cincinnati } = true | select the row whose enrollment record of all rows is maximum . the institution record of this row is university of cincinnati . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'enrollment_5': 5, 'institution_6': 6, 'university of cincinnati_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'enrollment_5': 'enrollment', 'institution_6': 'institution', 'university of cincinnati_7': 'university of cincinnati'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'enrollment_5': [0], 'institution_6': [1], 'university of cincinnati_7': [2]} | ['institution', 'nickname', 'location', 'founded', 'type', 'enrollment', 'joined', 'left'] | [['university of cincinnati', 'bearcats', 'cincinnati , ohio', '1819', 'public', '41357', '1975', '1991'], ['georgia institute of technology', 'yellow jackets', 'atlanta , georgia', '1885', 'public', '21557', '1975', '1978'], ['university of louisville', 'cardinals', 'louisville , kentucky', '1798', 'public', '22249', '1975', '1995'], ['university of memphis , 1', 'tigers', 'memphis , tennessee', '1912', 'public', '22365', '1975', '1991'], ['saint louis university', 'billikens', 'saint louis , missouri', '1818', 'private', '13785', '1975', '1982']] |
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/2-18974097-12.html.csv | comparative | the denver warriors had 40 fewer people enrolled than the wabash norsemen . | {'row_1': '3', 'row_2': '2', 'col': '4', 'col_other': '2,3', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '40', 'bigger': 'row2'}} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'mascot', 'warriors'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose mascot record fuzzily matches to warriors .', 'tostr': 'filter_eq { all_rows ; mascot ; warriors }'}, 'enrollment'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; mascot ; warriors } ; enrollment }', 'tointer': 'select the rows whose mascot record fuzzily matches to warriors . take the enrollment record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'mascot', 'norsemen'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose mascot record fuzzily matches to norsemen .', 'tostr': 'filter_eq { all_rows ; mascot ; norsemen }'}, 'enrollment'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; mascot ; norsemen } ; enrollment }', 'tointer': 'select the rows whose mascot record fuzzily matches to norsemen . take the enrollment record of this row .'}], 'result': '-40', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; mascot ; warriors } ; enrollment } ; hop { filter_eq { all_rows ; mascot ; norsemen } ; enrollment } }'}, '-40'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; mascot ; warriors } ; enrollment } ; hop { filter_eq { all_rows ; mascot ; norsemen } ; enrollment } } ; -40 }', 'tointer': 'select the rows whose mascot record fuzzily matches to warriors . take the enrollment record of this row . select the rows whose mascot record fuzzily matches to norsemen . take the enrollment record of this row . the second record is 40 larger than the first record .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'mascot', 'warriors'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose mascot record fuzzily matches to warriors .', 'tostr': 'filter_eq { all_rows ; mascot ; warriors }'}, 'location'], 'result': 'denver', 'ind': 6, 'tostr': 'hop { filter_eq { all_rows ; mascot ; warriors } ; location }'}, 'denver'], 'result': True, 'ind': 7, 'tostr': 'eq { hop { filter_eq { all_rows ; mascot ; warriors } ; location } ; denver }', 'tointer': 'the location record of the first row is denver .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'mascot', 'norsemen'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose mascot record fuzzily matches to norsemen .', 'tostr': 'filter_eq { all_rows ; mascot ; norsemen }'}, 'location'], 'result': 'wabash', 'ind': 8, 'tostr': 'hop { filter_eq { all_rows ; mascot ; norsemen } ; location }'}, 'wabash'], 'result': True, 'ind': 9, 'tostr': 'eq { hop { filter_eq { all_rows ; mascot ; norsemen } ; location } ; wabash }', 'tointer': 'the location record of the second row is wabash .'}], 'result': True, 'ind': 10, 'tostr': 'and { eq { hop { filter_eq { all_rows ; mascot ; warriors } ; location } ; denver } ; eq { hop { filter_eq { all_rows ; mascot ; norsemen } ; location } ; wabash } }', 'tointer': 'the location record of the first row is denver . the location record of the second row is wabash .'}], 'result': True, 'ind': 11, 'tostr': 'and { eq { diff { hop { filter_eq { all_rows ; mascot ; warriors } ; enrollment } ; hop { filter_eq { all_rows ; mascot ; norsemen } ; enrollment } } ; -40 } ; and { eq { hop { filter_eq { all_rows ; mascot ; warriors } ; location } ; denver } ; eq { hop { filter_eq { all_rows ; mascot ; norsemen } ; location } ; wabash } } } = true', 'tointer': 'select the rows whose mascot record fuzzily matches to warriors . take the enrollment record of this row . select the rows whose mascot record fuzzily matches to norsemen . take the enrollment record of this row . the second record is 40 larger than the first record . the location record of the first row is denver . the location record of the second row is wabash .'} | and { eq { diff { hop { filter_eq { all_rows ; mascot ; warriors } ; enrollment } ; hop { filter_eq { all_rows ; mascot ; norsemen } ; enrollment } } ; -40 } ; and { eq { hop { filter_eq { all_rows ; mascot ; warriors } ; location } ; denver } ; eq { hop { filter_eq { all_rows ; mascot ; norsemen } ; location } ; wabash } } } = true | select the rows whose mascot record fuzzily matches to warriors . take the enrollment record of this row . select the rows whose mascot record fuzzily matches to norsemen . take the enrollment record of this row . the second record is 40 larger than the first record . the location record of the first row is denver . the location record of the second row is wabash . | 14 | 12 | {'and_11': 11, 'result_12': 12, 'eq_5': 5, 'diff_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_13': 13, 'mascot_14': 14, 'warriors_15': 15, 'enrollment_16': 16, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_17': 17, 'mascot_18': 18, 'norsemen_19': 19, 'enrollment_20': 20, '-40_21': 21, 'and_10': 10, 'str_eq_7': 7, 'str_hop_6': 6, 'location_22': 22, 'denver_23': 23, 'str_eq_9': 9, 'str_hop_8': 8, 'location_24': 24, 'wabash_25': 25} | {'and_11': 'and', 'result_12': 'true', 'eq_5': 'eq', 'diff_4': 'diff', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_13': 'all_rows', 'mascot_14': 'mascot', 'warriors_15': 'warriors', 'enrollment_16': 'enrollment', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_17': 'all_rows', 'mascot_18': 'mascot', 'norsemen_19': 'norsemen', 'enrollment_20': 'enrollment', '-40_21': '-40', 'and_10': 'and', 'str_eq_7': 'str_eq', 'str_hop_6': 'str_hop', 'location_22': 'location', 'denver_23': 'denver', 'str_eq_9': 'str_eq', 'str_hop_8': 'str_hop', 'location_24': 'location', 'wabash_25': 'wabash'} | {'and_11': [12], 'result_12': [], 'eq_5': [11], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2, 6], 'all_rows_13': [0], 'mascot_14': [0], 'warriors_15': [0], 'enrollment_16': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3, 8], 'all_rows_17': [1], 'mascot_18': [1], 'norsemen_19': [1], 'enrollment_20': [3], '-40_21': [5], 'and_10': [11], 'str_eq_7': [10], 'str_hop_6': [7], 'location_22': [6], 'denver_23': [7], 'str_eq_9': [10], 'str_hop_8': [9], 'location_24': [8], 'wabash_25': [9]} | ['school', 'location', 'mascot', 'enrollment', 'ihsaa class', 'ihsaa football class', 'county'] | [['manchester', 'north manchester', 'squires', '432', 'aa', 'aa', '85 wabash'], ['northfield', 'wabash', 'norsemen', '389', 'aa', 'a', '85 wabash'], ['north miami', 'denver', 'warriors', '349', 'aa', 'a', '52 miami'], ['rochester community', 'rochester', 'zebras', '565', 'aa', 'aa', '25 fulton'], ['southwood', 'wabash', 'knights', '413', 'aa', 'a', '85 wabash'], ['tippecanoe valley', 'akron', 'vikings', '618', 'aaa', 'aaa', '43 kosciusko'], ['wabash', 'wabash', 'apachees', '447', 'aa', 'aa', '85 wabash'], ['whitko', 'south whitley', 'wildcats', '595', 'aaa', 'aaa', '92 whitley']] |
daigakk \ xc5 \ x8d | https://en.wikipedia.org/wiki/Daigakk%C5%8D | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11390711-1.html.csv | count | two of the daigakkō were provided by the japanese ministry of defense . | {'scope': 'all', 'criterion': 'equal', 'value': 'ministry of defense', 'result': '2', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'provider ( national government )', 'ministry of defense'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose provider ( national government ) record fuzzily matches to ministry of defense .', 'tostr': 'filter_eq { all_rows ; provider ( national government ) ; ministry of defense }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; provider ( national government ) ; ministry of defense } }', 'tointer': 'select the rows whose provider ( national government ) record fuzzily matches to ministry of defense . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; provider ( national government ) ; ministry of defense } } ; 2 } = true', 'tointer': 'select the rows whose provider ( national government ) record fuzzily matches to ministry of defense . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; provider ( national government ) ; ministry of defense } } ; 2 } = true | select the rows whose provider ( national government ) record fuzzily matches to ministry of defense . 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, 'provider (national government)_5': 5, 'ministry of defense_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', 'provider (national government)_5': 'provider ( national government )', 'ministry of defense_6': 'ministry of defense', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'provider (national government)_5': [0], 'ministry of defense_6': [0], '2_7': [2]} | ['english name', 'japanese orthography', 'pronouciation', 'abbreviation', 'provider ( national government )', 'foundation'] | [['japan coast guard academy', '海上保安大学校', 'kaijō hoan daigakkō', 'jcga', 'japan coast guard', '1951'], ['national college of nursing ( ko )', '国立看護大学校', 'kokuritsu kango daigakkō', 'ncn', 'national center for global health and medicine', '2001'], ['national defense academy of japan', '防衛大学校', 'bōei daigakkō', 'nda bōei - dai ( 防衛大 )', 'ministry of defense', '1952'], ['national defense medical college', '防衛医科大学校', 'bōei ika daigakkō', 'ndmc', 'ministry of defense', '1973'], ['meteorological college', '気象大学校', 'kishō daigakkō', 'mc ki - dai , kidaikō', 'japan meteorological agency', '1922']] |
list of generator rex episodes | https://en.wikipedia.org/wiki/List_of_Generator_Rex_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26982362-2.html.csv | ordinal | the first generator rex episode was directed by sam montes , it aired on 23rd april 2010 and was titled " the day that everything changed " . | {'scope': 'subset', 'row': '1', 'col': '5', 'order': '1', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'yes', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'sam montes'}} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'nth_min', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'directed by', 'sam montes'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; directed by ; sam montes }', 'tointer': 'select the rows whose directed by record fuzzily matches to sam montes .'}, 'original airdate', '1'], 'result': 'april 23 , 2010', 'ind': 1, 'tostr': 'nth_min { filter_eq { all_rows ; directed by ; sam montes } ; original airdate ; 1 }', 'tointer': 'select the rows whose directed by record fuzzily matches to sam montes . the 1st minimum original airdate record of these rows is april 23 , 2010 .'}, 'april 23 , 2010'], 'result': True, 'ind': 2, 'tostr': 'eq { nth_min { filter_eq { all_rows ; directed by ; sam montes } ; original airdate ; 1 } ; april 23 , 2010 }', 'tointer': 'select the rows whose directed by record fuzzily matches to sam montes . the 1st minimum original airdate record of these rows is april 23 , 2010 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'directed by', 'sam montes'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; directed by ; sam montes }', 'tointer': 'select the rows whose directed by record fuzzily matches to sam montes .'}, 'original airdate', '1'], 'result': None, 'ind': 3, 'tostr': 'nth_argmin { filter_eq { all_rows ; directed by ; sam montes } ; original airdate ; 1 }'}, 'title'], 'result': 'the day that everything changed', 'ind': 4, 'tostr': 'hop { nth_argmin { filter_eq { all_rows ; directed by ; sam montes } ; original airdate ; 1 } ; title }'}, 'the day that everything changed'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { nth_argmin { filter_eq { all_rows ; directed by ; sam montes } ; original airdate ; 1 } ; title } ; the day that everything changed }', 'tointer': 'the title record of the row with 1st minimum original airdate record is the day that everything changed .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { nth_min { filter_eq { all_rows ; directed by ; sam montes } ; original airdate ; 1 } ; april 23 , 2010 } ; eq { hop { nth_argmin { filter_eq { all_rows ; directed by ; sam montes } ; original airdate ; 1 } ; title } ; the day that everything changed } } = true', 'tointer': 'select the rows whose directed by record fuzzily matches to sam montes . the 1st minimum original airdate record of these rows is april 23 , 2010 . the title record of the row with 1st minimum original airdate record is the day that everything changed .'} | and { eq { nth_min { filter_eq { all_rows ; directed by ; sam montes } ; original airdate ; 1 } ; april 23 , 2010 } ; eq { hop { nth_argmin { filter_eq { all_rows ; directed by ; sam montes } ; original airdate ; 1 } ; title } ; the day that everything changed } } = true | select the rows whose directed by record fuzzily matches to sam montes . the 1st minimum original airdate record of these rows is april 23 , 2010 . the title record of the row with 1st minimum original airdate record is the day that everything changed . | 8 | 7 | {'and_6': 6, 'result_7': 7, 'eq_2': 2, 'nth_min_1': 1, 'filter_str_eq_0': 0, 'all_rows_8': 8, 'directed by_9': 9, 'sam montes_10': 10, 'original airdate_11': 11, '1_12': 12, 'april 23 , 2010_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'nth_argmin_3': 3, 'original airdate_14': 14, '1_15': 15, 'title_16': 16, 'the day that everything changed_17': 17} | {'and_6': 'and', 'result_7': 'true', 'eq_2': 'eq', 'nth_min_1': 'nth_min', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_8': 'all_rows', 'directed by_9': 'directed by', 'sam montes_10': 'sam montes', 'original airdate_11': 'original airdate', '1_12': '1', 'april 23 , 2010_13': 'april 23 , 2010', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'nth_argmin_3': 'nth_argmin', 'original airdate_14': 'original airdate', '1_15': '1', 'title_16': 'title', 'the day that everything changed_17': 'the day that everything changed'} | {'and_6': [7], 'result_7': [], 'eq_2': [6], 'nth_min_1': [2], 'filter_str_eq_0': [1, 3], 'all_rows_8': [0], 'directed by_9': [0], 'sam montes_10': [0], 'original airdate_11': [1], '1_12': [1], 'april 23 , 2010_13': [2], 'str_eq_5': [6], 'str_hop_4': [5], 'nth_argmin_3': [4], 'original airdate_14': [3], '1_15': [3], 'title_16': [4], 'the day that everything changed_17': [5]} | ['no in series', 'title', 'directed by', 'written by', 'original airdate', 'production code'] | [['1', 'the day that everything changed', 'sam montes', 'man of action', 'april 23 , 2010', '693 - 001'], ['2', 'string theory', 'rick morales', 'man of action', 'april 30 , 2010', '693 - 002'], ['3', 'beyond the sea', 'chris graham', 'man of action', 'may 7 , 2010', '693 - 003'], ['4', 'lockdown', 'sam montes', 'scott sonneborn', 'may 14 , 2010', '693 - 004'], ['5', 'the architect', 'rick morales', 'amy wolfram', 'may 21 , 2010', '693 - 005'], ['6', 'frostbite', 'chris graham', 'marty isenberg', 'may 28 , 2010', '693 - 006'], ['7', 'leader of the pack', 'sam montes', 'alexx van dyne', 'june 4 , 2010', '693 - 007'], ['8', 'breach', 'chris graham', 'adam beechen', 'june 11 , 2010', '693 - 009'], ['9', 'dark passage', 'sam montes', 'marsha griffin', 'june 18 , 2010', '693 - 010'], ['10', 'the forgotten', 'rick morales', 'paul giacoppo', 'september 17 , 2010', '693 - 011'], ['11', 'operation : wingman', 'chris graham', 'eugene son', 'september 24 , 2010', '693 - 012'], ['12', 'rabble', 'sam montes', 'rob hoegee', 'october 1 , 2010', '693 - 013'], ['13', 'the hunter', 'rick morales', 'michael ryan', 'october 8 , 2010', '693 - 008'], ['14', 'gravity', 'rick morales', 'andrew robinson', 'october 15 , 2010', '693 - 014'], ['15', 'what lies beneath', 'chris graham', 'marsha griffin', 'october 22 , 2010', '693 - 015'], ['16', 'the swarm', 'sam montes', 'paul giacoppo', 'october 29 , 2010', '693 - 016'], ['17', 'basic', 'rick morales', 'scott sonneborn', 'november 5 , 2010', '693 - 017'], ['18', 'plague', 'chris graham', 'tad stones', 'november 12 , 2010', '693 - 018'], ['19', 'promises , promises', 'sam montes', 'man of action', 'november 19 , 2010', '693 - 019'], ['20', 'badlands', 'rick morales', 'eugene son', 'december 3 , 2010', '693 - 021']] |
bh11960 | https://en.wikipedia.org/wiki/BH11960 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27155678-2.html.csv | unique | bartonella tribocorum is the only genus/species to have a gene name of alanyl - trna synthetase . | {'scope': 'all', 'row': '4', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'alanyl - trna synthetase', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'gene name', 'alanyl - trna synthetase'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose gene name record fuzzily matches to alanyl - trna synthetase .', 'tostr': 'filter_eq { all_rows ; gene name ; alanyl - trna synthetase }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; gene name ; alanyl - trna synthetase } }', 'tointer': 'select the rows whose gene name record fuzzily matches to alanyl - trna synthetase . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'gene name', 'alanyl - trna synthetase'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose gene name record fuzzily matches to alanyl - trna synthetase .', 'tostr': 'filter_eq { all_rows ; gene name ; alanyl - trna synthetase }'}, 'genus / species'], 'result': 'bartonella tribocorum', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; gene name ; alanyl - trna synthetase } ; genus / species }'}, 'bartonella tribocorum'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; gene name ; alanyl - trna synthetase } ; genus / species } ; bartonella tribocorum }', 'tointer': 'the genus / species record of this unqiue row is bartonella tribocorum .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; gene name ; alanyl - trna synthetase } } ; eq { hop { filter_eq { all_rows ; gene name ; alanyl - trna synthetase } ; genus / species } ; bartonella tribocorum } } = true', 'tointer': 'select the rows whose gene name record fuzzily matches to alanyl - trna synthetase . there is only one such row in the table . the genus / species record of this unqiue row is bartonella tribocorum .'} | and { only { filter_eq { all_rows ; gene name ; alanyl - trna synthetase } } ; eq { hop { filter_eq { all_rows ; gene name ; alanyl - trna synthetase } ; genus / species } ; bartonella tribocorum } } = true | select the rows whose gene name record fuzzily matches to alanyl - trna synthetase . there is only one such row in the table . the genus / species record of this unqiue row is bartonella tribocorum . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'gene name_7': 7, 'alanyl - trna synthetase_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'genus / species_9': 9, 'bartonella tribocorum_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'gene name_7': 'gene name', 'alanyl - trna synthetase_8': 'alanyl - trna synthetase', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'genus / species_9': 'genus / species', 'bartonella tribocorum_10': 'bartonella tribocorum'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'gene name_7': [0], 'alanyl - trna synthetase_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'genus / species_9': [2], 'bartonella tribocorum_10': [3]} | ['genus / species', 'gene name', 'accession number', 'sequence length', 'sequence similarity'] | [['bartonella henselae', 'hypothetical protein', 'bx897699 .1', '2805nt / 934aa', '100'], ['bartonella quintana', 'hypothetical protein', 'bx897700 .1', '2805nt / 934aa', '91'], ['bartonella grahamii', 'transcription regulator', 'cp001562 .1', '2799nt / 932aa', '87'], ['bartonella tribocorum', 'alanyl - trna synthetase', 'am260525 .1', '2799nt / 932aa', '87'], ['methylobacterium nodulans', 'hypothetical protein', 'yp_002500318 .1', '2820nt / 939aa', '53'], ['nitrobacter hamburgensis', 'double transmembrane region like', 'yp_578448 .1', '2817nt / 938aa', '53'], ['hyphomicrobium denitrificans', 'conserved hypothetical protein', 'zp_05374729 .1', '2973nt / 990aa', '53'], ['rhodopseudomonas palustris', 'double transmembrane region like', 'yp_568432 .1', '2826nt / 941aa', '54'], ['hoeflea phototrophica', 'double transmembrane region like', 'yp_002289983 .1', '1832nt / 943aa', '55']] |
pulp and paper industry | https://en.wikipedia.org/wiki/Pulp_and_paper_industry | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-293465-1.html.csv | aggregation | the main countries that are prominent in the pulp and paper industries produced a total of 290,133 tons of materials from raw wood in the year 2011 . | {'scope': 'all', 'col': '3', 'type': 'sum', 'result': '290,133', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'production in 2011 ( 1000 ton )'], 'result': '290,133', 'ind': 0, 'tostr': 'sum { all_rows ; production in 2011 ( 1000 ton ) }'}, '290,133'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; production in 2011 ( 1000 ton ) } ; 290,133 } = true', 'tointer': 'the sum of the production in 2011 ( 1000 ton ) record of all rows is 290,133 .'} | round_eq { sum { all_rows ; production in 2011 ( 1000 ton ) } ; 290,133 } = true | the sum of the production in 2011 ( 1000 ton ) record of all rows is 290,133 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'production in 2011 (1000 ton)_4': 4, '290,133_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'production in 2011 (1000 ton)_4': 'production in 2011 ( 1000 ton )', '290,133_5': '290,133'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'production in 2011 (1000 ton)_4': [0], '290,133_5': [1]} | ['rank 2011', 'country', 'production in 2011 ( 1000 ton )', 'share 2011', 'rank 2010', 'production in 2010 ( 1000 ton )'] | [['1', 'china', '99300', '24.9 %', '1', '92599'], ['2', 'united states', '75083', '18.8 %', '2', '75849'], ['3', 'japan', '26627', '6.7 %', '3', '27288'], ['4', 'germany', '22698', '5.7 %', '4', '23122'], ['5', 'canada', '12112', '3.0 %', '5', '12787'], ['6', 'south korea', '11492', '2.9 %', '8', '11120'], ['7', 'finland', '11329', '2.8 %', '6', '11789'], ['8', 'sweden', '11298', '2.8 %', '7', '11410'], ['9', 'brazil', '10159', '2.5 %', '10', '9796'], ['10', 'indonesia', '10035', '2.5 %', '9', '9951']] |
atlanta falcons draft history | https://en.wikipedia.org/wiki/Atlanta_Falcons_draft_history | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15198842-27.html.csv | majority | in the atlanta falcons draft history , for the players in the running back position , all of them were picked before round 10 . | {'scope': 'subset', 'col': '1', 'most_or_all': 'all', 'criterion': 'less_than', 'value': '10', 'subset': {'col': '5', 'criterion': 'equal', 'value': 'running back'}} | {'func': 'all_less', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', 'running back'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; position ; running back }', 'tointer': 'select the rows whose position record fuzzily matches to running back .'}, 'round', '10'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose position record fuzzily matches to running back . for the round records of these rows , all of them are less than 10 .', 'tostr': 'all_less { filter_eq { all_rows ; position ; running back } ; round ; 10 } = true'} | all_less { filter_eq { all_rows ; position ; running back } ; round ; 10 } = true | select the rows whose position record fuzzily matches to running back . for the round records of these rows , all of them are less than 10 . | 2 | 2 | {'all_less_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'position_4': 4, 'running back_5': 5, 'round_6': 6, '10_7': 7} | {'all_less_1': 'all_less', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'position_4': 'position', 'running back_5': 'running back', 'round_6': 'round', '10_7': '10'} | {'all_less_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'position_4': [0], 'running back_5': [0], 'round_6': [1], '10_7': [1]} | ['round', 'pick', 'overall', 'name', 'position', 'college'] | [['1', '8', '8', 'bob whitfield', 'offensive tackle', 'stanford'], ['1', '19', '19', 'tony smith', 'running back', 'southern miss'], ['2', '23', '51', 'chuck smith', 'defensive end', 'tennessee'], ['3', '17', '73', 'howard dinkins', 'linebacker', 'florida state'], ['4', '20', '104', 'frankie smith', 'cornerback', 'baylor'], ['6', '18', '158', 'terry ray', 'defensive back', 'oklahoma'], ['7', '14', '182', 'tim paulk', 'linebacker', 'florida'], ['8', '20', '216', 'derrick moore', 'running back', 'troy state'], ['8', '21', '217', 'reggie dwight', 'tight end', 'troy state'], ['9', '19', '243', 'keith alex', 'offensive tackle', 'texas a & m'], ['10', '18', '270', 'darryl hardy', 'linebacker', 'tennessee'], ['11', '17', '297', 'robin jones', 'defensive end', 'baylor']] |
statistics relating to enlargement of the european union | https://en.wikipedia.org/wiki/Statistics_relating_to_enlargement_of_the_European_Union | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1307842-6.html.csv | superlative | of the entries in the statistics relating to enlargement of the european union austria has the largest gdp per capita . | {'scope': 'all', 'col_superlative': '5', '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', 'gdp per capita ( us )'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; gdp per capita ( us ) }'}, 'member countries'], 'result': 'austria', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; gdp per capita ( us ) } ; member countries }'}, 'austria'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; gdp per capita ( us ) } ; member countries } ; austria } = true', 'tointer': 'select the row whose gdp per capita ( us ) record of all rows is maximum . the member countries record of this row is austria .'} | eq { hop { argmax { all_rows ; gdp per capita ( us ) } ; member countries } ; austria } = true | select the row whose gdp per capita ( us ) record of all rows is maximum . the member countries record of this row is austria . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'gdp per capita (us)_5': 5, 'member countries_6': 6, 'austria_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'gdp per capita (us)_5': 'gdp per capita ( us )', 'member countries_6': 'member countries', 'austria_7': 'austria'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'gdp per capita (us)_5': [0], 'member countries_6': [1], 'austria_7': [2]} | ['member countries', 'population', 'area ( km square )', 'gdp ( billion us )', 'gdp per capita ( us )'] | [['austria', '8206524', '83871', '145.238', '18048'], ['finland', '5261008', '338145', '80.955', '15859'], ['sweden', '9047752', '449964', '156.640', '17644'], ['accession countries', '22029977', '871980', '382.833', '17378'], ['existing members ( 1995 )', '350909402', '2495174', '5894.232', '16797']] |
golf magazine | https://en.wikipedia.org/wiki/Golf_Magazine | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11063491-1.html.csv | unique | pacific dunes in oregon is the only course designed after 2000 . | {'scope': 'all', 'row': '9', 'col': '5', 'col_other': '2,4', 'criterion': 'greater_than', 'value': '2000', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'designer , year', '2000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose designer , year record is greater than 2000 .', 'tostr': 'filter_greater { all_rows ; designer , year ; 2000 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; designer , year ; 2000 } }', 'tointer': 'select the rows whose designer , year record is greater than 2000 . there is only one such row in the table .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'designer , year', '2000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose designer , year record is greater than 2000 .', 'tostr': 'filter_greater { all_rows ; designer , year ; 2000 }'}, 'name'], 'result': 'pacific dunes', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; designer , year ; 2000 } ; name }'}, 'pacific dunes'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; designer , year ; 2000 } ; name } ; pacific dunes }', 'tointer': 'the name record of this unqiue row is pacific dunes .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'designer , year', '2000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose designer , year record is greater than 2000 .', 'tostr': 'filter_greater { all_rows ; designer , year ; 2000 }'}, 'state'], 'result': 'oregon', 'ind': 4, 'tostr': 'hop { filter_greater { all_rows ; designer , year ; 2000 } ; state }'}, 'oregon'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_greater { all_rows ; designer , year ; 2000 } ; state } ; oregon }', 'tointer': 'the state record of this unqiue row is oregon .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_greater { all_rows ; designer , year ; 2000 } ; name } ; pacific dunes } ; eq { hop { filter_greater { all_rows ; designer , year ; 2000 } ; state } ; oregon } }', 'tointer': 'the name record of this unqiue row is pacific dunes . the state record of this unqiue row is oregon .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_greater { all_rows ; designer , year ; 2000 } } ; and { eq { hop { filter_greater { all_rows ; designer , year ; 2000 } ; name } ; pacific dunes } ; eq { hop { filter_greater { all_rows ; designer , year ; 2000 } ; state } ; oregon } } } = true', 'tointer': 'select the rows whose designer , year record is greater than 2000 . there is only one such row in the table . the name record of this unqiue row is pacific dunes . the state record of this unqiue row is oregon .'} | and { only { filter_greater { all_rows ; designer , year ; 2000 } } ; and { eq { hop { filter_greater { all_rows ; designer , year ; 2000 } ; name } ; pacific dunes } ; eq { hop { filter_greater { all_rows ; designer , year ; 2000 } ; state } ; oregon } } } = true | select the rows whose designer , year record is greater than 2000 . there is only one such row in the table . the name record of this unqiue row is pacific dunes . the state record of this unqiue row is oregon . | 10 | 8 | {'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_9': 9, 'designer , year_10': 10, '2000_11': 11, 'and_6': 6, 'str_eq_3': 3, 'str_hop_2': 2, 'name_12': 12, 'pacific dunes_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'state_14': 14, 'oregon_15': 15} | {'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_9': 'all_rows', 'designer , year_10': 'designer , year', '2000_11': '2000', 'and_6': 'and', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_12': 'name', 'pacific dunes_13': 'pacific dunes', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'state_14': 'state', 'oregon_15': 'oregon'} | {'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_greater_0': [1, 2, 4], 'all_rows_9': [0], 'designer , year_10': [0], '2000_11': [0], 'and_6': [7], 'str_eq_3': [6], 'str_hop_2': [3], 'name_12': [2], 'pacific dunes_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'state_14': [4], 'oregon_15': [5]} | ['rank', 'name', 'location', 'state', 'designer , year'] | [['1', 'pine valley', 'pine valley', 'new jersey', 'george crump / harry colt , 1918'], ['2', 'cypress point', 'pebble beach', 'california', 'alister mackenzie , 1918'], ['3', 'augusta national', 'augusta', 'georgia', 'alister mackenzie / bobby jones , 1933'], ['4', 'pebble beach', 'pebble beach', 'california', 'jack neville / douglas grant , 1919'], ['5', 'shinnecock hills', 'southampton', 'new york', 'william flynn , 1931'], ['6', 'oakmont', 'oakmont', 'pennsylvania', 'henry fownes , 1903'], ['7', 'merion ( east )', 'ardmore', 'pennsylvania', 'hugh wilson , 1912'], ['8', 'sand hills', 'mullen', 'nebraska', 'bill coore / ben crenshaw , 1994'], ['9', 'pacific dunes', 'bandon', 'oregon', 'tom doak , 2001'], ['10', 'national golf links of america', 'southampton', 'new york', 'charles b macdonald , 1911']] |
1947 vfl season | https://en.wikipedia.org/wiki/1947_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10809444-2.html.csv | superlative | in the 1947 vfl season , the game that had the highest number of spectators was the south melbourne vs. carlton game . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '6', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1,3', 'subset': None} | {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'crowd'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; crowd }'}, 'home team'], 'result': 'south melbourne', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; crowd } ; home team }'}, 'south melbourne'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; crowd } ; home team } ; south melbourne }', 'tointer': 'select the row whose crowd record of all rows is maximum . the home team record of this row is south melbourne .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'crowd'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; crowd }'}, 'away team'], 'result': 'carlton', 'ind': 3, 'tostr': 'hop { argmax { all_rows ; crowd } ; away team }'}, 'carlton'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { argmax { all_rows ; crowd } ; away team } ; carlton }', 'tointer': 'the away team record of this row is carlton .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { hop { argmax { all_rows ; crowd } ; home team } ; south melbourne } ; eq { hop { argmax { all_rows ; crowd } ; away team } ; carlton } } = true', 'tointer': 'select the row whose crowd record of all rows is maximum . the home team record of this row is south melbourne . the away team record of this row is carlton .'} | and { eq { hop { argmax { all_rows ; crowd } ; home team } ; south melbourne } ; eq { hop { argmax { all_rows ; crowd } ; away team } ; carlton } } = true | select the row whose crowd record of all rows is maximum . the home team record of this row is south melbourne . the away team record of this row is carlton . | 7 | 6 | {'and_5': 5, 'result_6': 6, 'str_eq_2': 2, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_7': 7, 'crowd_8': 8, 'home team_9': 9, 'south melbourne_10': 10, 'str_eq_4': 4, 'str_hop_3': 3, 'away team_11': 11, 'carlton_12': 12} | {'and_5': 'and', 'result_6': 'true', 'str_eq_2': 'str_eq', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_7': 'all_rows', 'crowd_8': 'crowd', 'home team_9': 'home team', 'south melbourne_10': 'south melbourne', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'away team_11': 'away team', 'carlton_12': 'carlton'} | {'and_5': [6], 'result_6': [], 'str_eq_2': [5], 'str_hop_1': [2], 'argmax_0': [1, 3], 'all_rows_7': [0], 'crowd_8': [0], 'home team_9': [1], 'south melbourne_10': [2], 'str_eq_4': [5], 'str_hop_3': [4], 'away team_11': [3], 'carlton_12': [4]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['north melbourne', '8.11 ( 59 )', 'st kilda', '8.13 ( 61 )', 'arden street oval', '8000', '26 april 1947'], ['fitzroy', '13.22 ( 100 )', 'richmond', '11.11 ( 77 )', 'brunswick street oval', '22000', '26 april 1947'], ['melbourne', '14.25 ( 109 )', 'geelong', '11.7 ( 73 )', 'mcg', '12000', '26 april 1947'], ['footscray', '15.13 ( 103 )', 'essendon', '13.11 ( 89 )', 'western oval', '22000', '26 april 1947'], ['hawthorn', '13.9 ( 87 )', 'collingwood', '19.20 ( 134 )', 'glenferrie oval', '15000', '26 april 1947'], ['south melbourne', '12.12 ( 84 )', 'carlton', '9.16 ( 70 )', 'lake oval', '30000', '26 april 1947']] |
wheelchair tennis at the 2008 summer paralympics | https://en.wikipedia.org/wiki/Wheelchair_tennis_at_the_2008_Summer_Paralympics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18403681-1.html.csv | superlative | the most silver medals won in wheelchair tennis at the 2008 summer paralympics was by the netherlands . | {'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', 'silver'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; silver }'}, 'nation'], 'result': 'netherlands ( ned )', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; silver } ; nation }'}, 'netherlands ( ned )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; silver } ; nation } ; netherlands ( ned ) } = true', 'tointer': 'select the row whose silver record of all rows is maximum . the nation record of this row is netherlands ( ned ) .'} | eq { hop { argmax { all_rows ; silver } ; nation } ; netherlands ( ned ) } = true | select the row whose silver record of all rows is maximum . the nation record of this row is netherlands ( ned ) . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'silver_5': 5, 'nation_6': 6, 'netherlands (ned)_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'silver_5': 'silver', 'nation_6': 'nation', 'netherlands (ned)_7': 'netherlands ( ned )'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'silver_5': [0], 'nation_6': [1], 'netherlands (ned)_7': [2]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'netherlands ( ned )', '2', '3', '1', '6'], ['2', 'france ( fra )', '1', '0', '2', '3'], ['3', 'great britain ( gbr )', '1', '0', '1', '2'], ['3', 'japan ( jpn )', '1', '0', '1', '2'], ['3', 'united states ( usa )', '1', '0', '1', '2'], ['6', 'sweden ( swe )', '0', '2', '0', '2'], ['7', 'israel ( isr )', '0', '1', '0', '1'], ['total', 'total', '6', '6', '6', '18']] |
atp bordeaux | https://en.wikipedia.org/wiki/ATP_Bordeaux | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16028631-1.html.csv | count | in the atp bordeaux , guy forget was the champion on two occasions . | {'scope': 'all', 'criterion': 'equal', 'value': 'guy forget', 'result': '2', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'champions', 'guy forget'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose champions record fuzzily matches to guy forget .', 'tostr': 'filter_eq { all_rows ; champions ; guy forget }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; champions ; guy forget } }', 'tointer': 'select the rows whose champions record fuzzily matches to guy forget . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; champions ; guy forget } } ; 2 } = true', 'tointer': 'select the rows whose champions record fuzzily matches to guy forget . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; champions ; guy forget } } ; 2 } = true | select the rows whose champions record fuzzily matches to guy forget . 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, 'champions_5': 5, 'guy forget_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', 'champions_5': 'champions', 'guy forget_6': 'guy forget', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'champions_5': [0], 'guy forget_6': [0], '2_7': [2]} | ['year', 'tournament name', 'champions', 'runners - up', 'score'] | [['1979', 'grand prix passing shot', 'yannick noah', 'harold solomon', '6 - 0 , 6 - 7 , 6 - 1 , 1 - 6 , 6 - 4'], ['1980', 'grand prix de passing shot', 'mario martinez', 'gianni ocleppo', '6 - 0 , 7 - 5 , 7 - 5'], ['1981', 'grand prix passing shot', 'andrés gómez', 'thierry tulasne', '7 - 6 , 7 - 6 , 6 - 1'], ['1982', 'grand prix passing shot', 'hans gildemeister', 'pablo arraya', '7 - 5 , 6 - 1'], ['1983', 'grand prix passing shot', 'pablo arraya', 'juan aguilera', '7 - 5 , 7 - 5'], ['1984', 'grand prix passing shot', 'josé higueras', 'francesco cancellotti', '7 - 6 , 6 - 1'], ['1985', 'nabisco grand prix passing shot', 'diego pérez', 'jimmy brown', '6 - 4 , 7 - 6'], ['1986', 'nabisco grand prix passing shot', 'paolo canè', 'kent carlsson', '6 - 4 , 1 - 6 , 7 - 5'], ['1987', 'nabisco grand prix passing shot', 'emilio sánchez', 'ronald agénor', '5 - 7 , 6 - 4 , 6 - 4'], ['1988', 'ngp passing shot de bordeaux', 'thomas muster', 'ronald agénor', '6 - 3 , 6 - 3'], ['1989', 'grand prix passing shot de bordeaux', 'ivan lendl', 'emilio sánchez', '6 - 2 , 6 - 2'], ['1990', 'grand prix passing shot', 'guy forget', 'goran ivanišević', '6 - 4 , 6 - 3'], ['1991', 'grand prix passing shot', 'guy forget', 'olivier delaître', '6 - 1 , 6 - 3'], ['1992', 'grand prix passing shot', 'andrei medvedev', 'sergi bruguera', '6 - 3 , 1 - 6 , 6 - 2'], ['1993', 'grand prix passing shot bordeaux', 'sergi bruguera', 'diego nargiso', '7 - 5 , 6 - 2'], ['1994', 'grand prix passing shot', 'wayne ferreira', 'jeff tarango', '6 - 0 , 7 - 5'], ['1995', 'grand prix passing shot bordeaux', 'yahiya doumbia', 'jakob hlasek', '6 - 4 , 6 - 4']] |
2007 - 08 detroit pistons season | https://en.wikipedia.org/wiki/2007%E2%80%9308_Detroit_Pistons_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11960944-10.html.csv | majority | the detroit pistons won almost all of the games in its ' 07 -- '08 season . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'w', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'score', 'w'], 'result': True, 'ind': 0, 'tointer': 'for the score records of all rows , most of them fuzzily match to w .', 'tostr': 'most_eq { all_rows ; score ; w } = true'} | most_eq { all_rows ; score ; w } = true | for the score records of all rows , most of them fuzzily match to w . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'score_3': 3, 'w_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'score_3': 'score', 'w_4': 'w'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'score_3': [0], 'w_4': [0]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'series'] | [['1', 'may 3', 'orlando', 'w 91 - 72', 'billups ( 19 )', 'maxiell ( 9 )', 'billups ( 7 )', 'the palace of auburn hills 22076', '1 - 0'], ['2', 'may 5', 'orlando', 'w 100 - 93', 'billups ( 28 )', 'prince ( 10 )', 'prince ( 5 )', 'the palace of auburn hills 22076', '2 - 0'], ['3', 'may 7', 'orlando', 'l 111 - 86', 'hamilton ( 24 )', 'prince ( 7 )', 'hamilton , prince ( 3 )', 'amway arena 17519', '2 - 1'], ['4', 'may 10', 'orlando', 'w 90 - 89', 'hamilton ( 32 )', 'mcdyess ( 14 )', 'prince ( 5 )', 'amway arena 17519', '3 - 1'], ['5', 'may 13', 'orlando', 'w 91 - 86', 'hamilton ( 31 )', 'mcdyess ( 11 )', 'stuckey ( 6 )', 'the palace of auburn hills 22076', '4 - 1']] |
2009 thailand national games | https://en.wikipedia.org/wiki/2009_Thailand_National_Games | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18615220-1.html.csv | count | two provinces in thailand won exactly 13 gold medals in the 2009 thailand national games . | {'scope': 'all', 'criterion': 'equal', 'value': '13', 'result': '2', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'gold', '13'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose gold record is equal to 13 .', 'tostr': 'filter_eq { all_rows ; gold ; 13 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; gold ; 13 } }', 'tointer': 'select the rows whose gold record is equal to 13 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; gold ; 13 } } ; 2 } = true', 'tointer': 'select the rows whose gold record is equal to 13 . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; gold ; 13 } } ; 2 } = true | select the rows whose gold record is equal to 13 . 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, 'gold_5': 5, '13_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'gold_5': 'gold', '13_6': '13', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'gold_5': [0], '13_6': [0], '2_7': [2]} | ['rank', 'province', 'gold', 'silver', 'bronze', 'total'] | [['1', 'bangkok', '129', '114', '80', '323'], ['2', 'suphan buri', '39', '24', '30', '93'], ['3', 'trang', '36', '15', '28', '79'], ['4', 'chonburi', '30', '40', '32', '102'], ['5', 'nakhon ratchasima', '17', '22', '29', '68'], ['6', 'chiang mai', '16', '25', '38', '79'], ['7', 'nonthaburi', '13', '13', '21', '47'], ['8', 'si sa ket', '13', '5', '14', '32'], ['9', 'ubon ratchathani', '12', '8', '25', '45'], ['10', 'samut sakhon', '10', '9', '11', '30']] |
1971 icf canoe sprint world championships | https://en.wikipedia.org/wiki/1971_ICF_Canoe_Sprint_World_Championships | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18567469-4.html.csv | unique | the soviet union was the only nation to win seven gold medals . | {'scope': 'all', 'row': '1', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': '7', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'gold', '7'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose gold record is equal to 7 .', 'tostr': 'filter_eq { all_rows ; gold ; 7 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; gold ; 7 } }', 'tointer': 'select the rows whose gold record is equal to 7 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'gold', '7'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose gold record is equal to 7 .', 'tostr': 'filter_eq { all_rows ; gold ; 7 }'}, 'nation'], 'result': 'soviet union', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; gold ; 7 } ; nation }'}, 'soviet union'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; gold ; 7 } ; nation } ; soviet union }', 'tointer': 'the nation record of this unqiue row is soviet union .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; gold ; 7 } } ; eq { hop { filter_eq { all_rows ; gold ; 7 } ; nation } ; soviet union } } = true', 'tointer': 'select the rows whose gold record is equal to 7 . there is only one such row in the table . the nation record of this unqiue row is soviet union .'} | and { only { filter_eq { all_rows ; gold ; 7 } } ; eq { hop { filter_eq { all_rows ; gold ; 7 } ; nation } ; soviet union } } = true | select the rows whose gold record is equal to 7 . there is only one such row in the table . the nation record of this unqiue row is soviet union . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'gold_7': 7, '7_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'nation_9': 9, 'soviet union_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'gold_7': 'gold', '7_8': '7', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'nation_9': 'nation', 'soviet union_10': 'soviet union'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'gold_7': [0], '7_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'nation_9': [2], 'soviet union_10': [3]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'soviet union', '7', '2', '6', '15'], ['2', 'hungary', '4', '5', '2', '11'], ['3', 'romania', '2', '2', '5', '9'], ['4', 'west germany', '2', '2', '1', '5'], ['5', 'east germany', '1', '1', '2', '4'], ['6', 'sweden', '1', '1', '0', '2'], ['7', 'bulgaria', '0', '0', '2', '2'], ['8', 'poland', '1', '0', '0', '1'], ['9', 'austria', '0', '1', '0', '1'], ['10', 'belgium', '0', '1', '0', '1'], ['11', 'czechoslovakia', '0', '1', '0', '1'], ['12', 'netherlands', '0', '1', '0', '1'], ['13', 'norway', '0', '1', '0', '1'], ['total', 'total', '18', '18', '18', '54']] |
wru division five west | https://en.wikipedia.org/wiki/WRU_Division_Five_West | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17941032-1.html.csv | aggregation | wru division five west clubs accumulated a total of 640 points across all teams . | {'scope': 'all', 'col': '12', 'type': 'sum', 'result': '640', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'points'], 'result': '640', 'ind': 0, 'tostr': 'sum { all_rows ; points }'}, '640'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; points } ; 640 } = true', 'tointer': 'the sum of the points record of all rows is 640 .'} | round_eq { sum { all_rows ; points } ; 640 } = true | the sum of the points record of all rows is 640 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'points_4': 4, '640_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'points_4': 'points', '640_5': '640'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'points_4': [0], '640_5': [1]} | ['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'], ['neyland rfc', '22', '20', '1', '1', '980', '174', '148', '19', '16', '0', '98'], ['aberaeron rfc', '22', '19', '1', '2', '889', '152', '136', '19', '14', '0', '92'], ['fishguard and goodwick rfc', '22', '17', '1', '4', '618', '198', '80', '20', '8', '3', '81'], ['furnace united rfc', '22', '14', '0', '8', '655', '243', '105', '27', '11', '6', '73'], ['penygroes rfc', '22', '11', '0', '11', '460', '391', '61', '54', '7', '6', '57'], ['new dock stars rfc', '22', '11', '0', '11', '592', '634', '90', '92', '9', '2', '55'], ['st clears rfc', '22', '10', '1', '11', '408', '398', '55', '49', '6', '3', '51'], ['st davids rfc', '22', '11', '0', '11', '351', '546', '50', '80', '5', '1', '50'], ['bynea rfc', '22', '10', '0', '12', '261', '452', '28', '60', '1', '0', '41'], ['llangwm rfc', '22', '3', '0', '19', '273', '784', '31', '119', '2', '7', '21'], ['swansea uplands rfc', '22', '2', '0', '20', '148', '916', '22', '140', '2', '1', '11'], ['pontyates rfc', '22', '2', '0', '20', '190', '937', '17', '144', '1', '1', '10']] |
electric car | https://en.wikipedia.org/wiki/Electric_car | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-16105186-2.html.csv | unique | denver is the only city with over 350 g / mi . | {'scope': 'all', 'row': '6', 'col': '10', 'col_other': 'n/a', 'criterion': 'greater_than', 'value': '350 g / mi', 'subset': None} | {'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'rocky mountains ( denver )', '350 g / mi'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose rocky mountains ( denver ) record is greater than 350 g / mi .', 'tostr': 'filter_greater { all_rows ; rocky mountains ( denver ) ; 350 g / mi }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; rocky mountains ( denver ) ; 350 g / mi } } = true', 'tointer': 'select the rows whose rocky mountains ( denver ) record is greater than 350 g / mi . there is only one such row in the table .'} | only { filter_greater { all_rows ; rocky mountains ( denver ) ; 350 g / mi } } = true | select the rows whose rocky mountains ( denver ) record is greater than 350 g / mi . there is only one such row in the table . | 2 | 2 | {'only_1': 1, 'result_2': 2, 'filter_greater_0': 0, 'all_rows_3': 3, 'rocky mountains (denver)_4': 4, '350 g/ mi_5': 5} | {'only_1': 'only', 'result_2': 'true', 'filter_greater_0': 'filter_greater', 'all_rows_3': 'all_rows', 'rocky mountains (denver)_4': 'rocky mountains ( denver )', '350 g/ mi_5': '350 g / mi'} | {'only_1': [2], 'result_2': [], 'filter_greater_0': [1], 'all_rows_3': [0], 'rocky mountains (denver)_4': [0], '350 g/ mi_5': [0]} | ['vehicle', 'epa rated all - electric range', 'epa rated combined fuel economy', 'alaska ( juneau )', 'california ( san francisco )', 'mid - atlantic south ( washington , dc )', 'us national average electric mix', 'southeast ( atlanta )', 'midwest ( des moines )', 'rocky mountains ( denver )'] | [['mitsubishi i - miev', '-', '112 mpg - e ( 30 kw - hrs / 100 miles )', '80 g / mi ( 50 g / km )', '100 g / mi ( 62 g / km )', '160 g / mi ( 99 g / km )', '200 g / mi ( 124 g / km )', '230 g / mi ( 143 g / km )', '270 g / mi ( 168 g / km )', '290 g / mi ( 180 g / km )'], ['ford focus electric', '-', '105 mpg - e ( 32 kw - hrs / 100 miles )', '80 g / mi ( 50 g / km )', '110 g / mi ( 68 g / km )', '170 g / mi ( 106 g / km )', '210 g / mi ( 131 g / km )', '250 g / mi ( 155 g / km )', '280 g / mi ( 174 g / km )', '310 g / mi ( 193 g / km )'], ['bmw activee', '-', '102 mpg - e ( 33 kw - hrs / 100 miles )', '90 g / mi ( 56 g / km )', '110 g / mi ( 68 g / km )', '180 g / mi ( 112 g / km )', '220 g / mi ( 137 g / km )', '250 g / mi ( 155 g / km )', '290 g / mi ( 180 g / km )', '320 g / mi ( 199 g / km )'], ['nissan leaf', '-', '99 mpg - e ( 34 kw - hrs / 100 miles )', '90 g / mi ( 56 g / km )', '120 g / mi ( 75 g / km )', '190 g / mi ( 118 g / km )', '230 g / mi ( 143 g / km )', '260 g / mi ( 162 g / km )', '300 g / mi ( 186 g / km )', '330 g / mi ( 205 g / km )'], ['chevrolet volt', '-', '94 mpg - e ( 36 kw - hrs / 100 miles )', '170 g / mi ( 106 g / km ) ( 1 )', '190 g / mi ( 118 g / km ) ( 1 )', '230 g / mi ( 143 g / km ) ( 1 )', '260 g / mi ( 162 g / km ) ( 1 )', '290 g / mi ( 180 g / km ) ( 1 )', '310 g / mi ( 193 g / km ) ( 1 )', '330 g / mi ( 205 g / km ) 1 )'], ['smart ed', '-', '87 mpg - e ( 39 kw - hrs / 100 miles )', '100 g / mi ( 62 g / km )', '130 g / mi ( 81 g / km )', '210 g / mi ( 131 g / km )', '260 g / mi ( 162 g / km )', '300 g / mi ( 186 g / km )', '350 g / mi ( 218 g / km )', '380 g / mi ( 236 g / km )']] |
test matches ( 1991 - 2000 ) | https://en.wikipedia.org/wiki/Test_matches_%281991%E2%80%932000%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12410929-9.html.csv | count | two of the test matches took place in the month of january in 1992 . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'january 1992', 'result': '2', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'january 1992'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to january 1992 .', 'tostr': 'filter_eq { all_rows ; date ; january 1992 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; date ; january 1992 } }', 'tointer': 'select the rows whose date record fuzzily matches to january 1992 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; date ; january 1992 } } ; 2 } = true', 'tointer': 'select the rows whose date record fuzzily matches to january 1992 . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; date ; january 1992 } } ; 2 } = true | select the rows whose date record fuzzily matches to january 1992 . 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, 'date_5': 5, 'january 1992_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', 'date_5': 'date', 'january 1992_6': 'january 1992', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'date_5': [0], 'january 1992_6': [0], '2_7': [2]} | ['date', 'home captain', 'away captain', 'venue', 'result'] | [['29 , 30 november , 1 , 2 december 1991', 'allan border', 'mohammad azharuddin', 'brisbane cricket ground', 'aus by 10 wkts'], ['26 , 27 , 28 , 29 december 1991', 'allan border', 'mohammad azharuddin', 'melbourne cricket ground', 'aus by 8 wkts'], ['2 , 3 , 4 , 5 , 6 january 1992', 'allan border', 'mohammad azharuddin', 'sydney cricket ground', 'draw'], ['25 , 26 , 27 , 28 , 29 january 1992', 'allan border', 'mohammad azharuddin', 'adelaide oval', 'aus by 38 runs'], ['1 , 2 , 3 , 4 , 5 february 1992', 'allan border', 'mohammad azharuddin', 'waca ground', 'aus by 300 runs']] |
max biaggi | https://en.wikipedia.org/wiki/Max_Biaggi | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1694580-3.html.csv | superlative | max biaggi had the least amount of races in the year of 2011 . | {'scope': 'all', 'col_superlative': '2', 'row_superlative': '5', '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', 'race'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; race }'}, 'season'], 'result': '2011', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; race } ; season }'}, '2011'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; race } ; season } ; 2011 } = true', 'tointer': 'select the row whose race record of all rows is minimum . the season record of this row is 2011 .'} | eq { hop { argmin { all_rows ; race } ; season } ; 2011 } = true | select the row whose race record of all rows is minimum . the season record of this row is 2011 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'race_5': 5, 'season_6': 6, '2011_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'race_5': 'race', 'season_6': 'season', '2011_7': '2011'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'race_5': [0], 'season_6': [1], '2011_7': [2]} | ['season', 'race', 'podium', 'pole', 'flap'] | [['2007', '25', '17', '0', '5'], ['2008', '28', '7', '0', '1'], ['2009', '28', '9', '0', '1'], ['2010', '26', '14', '2', '2'], ['2011', '21', '12', '2', '5'], ['2012', '27', '11', '1', '5'], ['total', '155', '70', '5', '19']] |
2007 latvian higher league | https://en.wikipedia.org/wiki/2007_Latvian_Higher_League | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11236683-2.html.csv | count | only two teams had 18 wins in the 2007 latvian higher league . | {'scope': 'all', 'criterion': 'equal', 'value': '18', 'result': '2', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'wins', '18'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose wins record is equal to 18 .', 'tostr': 'filter_eq { all_rows ; wins ; 18 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; wins ; 18 } }', 'tointer': 'select the rows whose wins record is equal to 18 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; wins ; 18 } } ; 2 } = true', 'tointer': 'select the rows whose wins record is equal to 18 . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; wins ; 18 } } ; 2 } = true | select the rows whose wins record is equal to 18 . 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, 'wins_5': 5, '18_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'wins_5': 'wins', '18_6': '18', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'wins_5': [0], '18_6': [0], '2_7': [2]} | ['position', 'club', 'played', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'points', 'goal difference'] | [['1', 'fk ventspils', '28', '18', '6', '4', '59', '16', '60', '+ 43'], ['2', 'fhk liepājas metalurgs', '28', '18', '4', '6', '42', '21', '58', '+ 21'], ['3', 'fk rīga', '28', '17', '6', '5', '48', '28', '57', '+ 20'], ['4', 'skonto fc rīga', '28', '16', '7', '5', '54', '27', '55', '+ 27'], ['5', 'fk daugava daugavpils', '28', '9', '6', '13', '33', '38', '33', '- 5'], ['6', 'fk jūrmala', '28', '7', '5', '16', '28', '51', '26', '- 23'], ['7', 'dinaburg fc daugavpils', '28', '6', '2', '20', '23', '58', '20', '- 35'], ['8', 'jfc olimps rīga', '28', '2', '2', '24', '15', '63', '8', '- 48']] |
nordic skiing | https://en.wikipedia.org/wiki/Nordic_skiing | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-174491-6.html.csv | comparative | klavdija bojarskikh participated in the winter olympics for skiing earlier than toini gustafsson . | {'row_1': '2', 'row_2': '3', 'col': '3', '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', 'winner', 'klavdija bojarskikh'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winner record fuzzily matches to klavdija bojarskikh .', 'tostr': 'filter_eq { all_rows ; winner ; klavdija bojarskikh }'}, 'winter olympics'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; winner ; klavdija bojarskikh } ; winter olympics }', 'tointer': 'select the rows whose winner record fuzzily matches to klavdija bojarskikh . take the winter olympics record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winner', 'toini gustafsson'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose winner record fuzzily matches to toini gustafsson .', 'tostr': 'filter_eq { all_rows ; winner ; toini gustafsson }'}, 'winter olympics'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; winner ; toini gustafsson } ; winter olympics }', 'tointer': 'select the rows whose winner record fuzzily matches to toini gustafsson . take the winter olympics record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; winner ; klavdija bojarskikh } ; winter olympics } ; hop { filter_eq { all_rows ; winner ; toini gustafsson } ; winter olympics } } = true', 'tointer': 'select the rows whose winner record fuzzily matches to klavdija bojarskikh . take the winter olympics record of this row . select the rows whose winner record fuzzily matches to toini gustafsson . take the winter olympics record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; winner ; klavdija bojarskikh } ; winter olympics } ; hop { filter_eq { all_rows ; winner ; toini gustafsson } ; winter olympics } } = true | select the rows whose winner record fuzzily matches to klavdija bojarskikh . take the winter olympics record of this row . select the rows whose winner record fuzzily matches to toini gustafsson . take the winter olympics 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, 'winner_7': 7, 'klavdija bojarskikh_8': 8, 'winter olympics_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'winner_11': 11, 'toini gustafsson_12': 12, 'winter olympics_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', 'winner_7': 'winner', 'klavdija bojarskikh_8': 'klavdija bojarskikh', 'winter olympics_9': 'winter olympics', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'winner_11': 'winner', 'toini gustafsson_12': 'toini gustafsson', 'winter olympics_13': 'winter olympics'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'winner_7': [0], 'klavdija bojarskikh_8': [0], 'winter olympics_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'winner_11': [1], 'toini gustafsson_12': [1], 'winter olympics_13': [3]} | ['winner', 'country', 'winter olympics', 'fis nordic world ski championships', 'holmenkollen'] | [['ljubov kozyreva', 'soviet union', '1956', '1954 , 1956', '1955'], ['klavdija bojarskikh', 'soviet union', '1964', '1964 , 1966', '1965 , 1966'], ['toini gustafsson', 'sweden', '1968', '1968', '1960 , 1967 , 1968'], ['galina kulakova', 'soviet union', '1972', '1972 , 1974', '1970 , 1979'], ['raisa smetanina', 'soviet union', '1976', '1976', '1975']] |
bharatiya janata party | https://en.wikipedia.org/wiki/Bharatiya_Janata_Party | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-149330-1.html.csv | comparative | the bharatiya janata party received a higher percentage of votes in the 10th lok sabha general election than the 8th lok sabha general election . | {'row_1': '4', 'row_2': '2', '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', 'general election', '10th lok sabha'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose general election record fuzzily matches to 10th lok sabha .', 'tostr': 'filter_eq { all_rows ; general election ; 10th lok sabha }'}, '% of votes'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; general election ; 10th lok sabha } ; % of votes }', 'tointer': 'select the rows whose general election record fuzzily matches to 10th lok sabha . take the % of votes record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'general election', '8th lok sabha'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose general election record fuzzily matches to 8th lok sabha .', 'tostr': 'filter_eq { all_rows ; general election ; 8th lok sabha }'}, '% of votes'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; general election ; 8th lok sabha } ; % of votes }', 'tointer': 'select the rows whose general election record fuzzily matches to 8th lok sabha . take the % of votes record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; general election ; 10th lok sabha } ; % of votes } ; hop { filter_eq { all_rows ; general election ; 8th lok sabha } ; % of votes } } = true', 'tointer': 'select the rows whose general election record fuzzily matches to 10th lok sabha . take the % of votes record of this row . select the rows whose general election record fuzzily matches to 8th lok sabha . take the % of votes record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; general election ; 10th lok sabha } ; % of votes } ; hop { filter_eq { all_rows ; general election ; 8th lok sabha } ; % of votes } } = true | select the rows whose general election record fuzzily matches to 10th lok sabha . take the % of votes record of this row . select the rows whose general election record fuzzily matches to 8th lok sabha . take the % of votes 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, 'general election_7': 7, '10th lok sabha_8': 8, '% of votes_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'general election_11': 11, '8th lok sabha_12': 12, '% of votes_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', 'general election_7': 'general election', '10th lok sabha_8': '10th lok sabha', '% of votes_9': '% of votes', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'general election_11': 'general election', '8th lok sabha_12': '8th lok sabha', '% of votes_13': '% of votes'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'general election_7': [0], '10th lok sabha_8': [0], '% of votes_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'general election_11': [1], '8th lok sabha_12': [1], '% of votes_13': [3]} | ['year', 'general election', 'seats won', 'change in seat', '% of votes', 'votes swing'] | [['indian general election , 1980', '7th lok sabha', '12', '12', '8.75 %', '8.75'], ['indian general election , 1984', '8th lok sabha', '2', '10', '7.74 %', '1.01'], ['indian general election , 1989', '9th lok sabha', '85', '83', '11.36', '3.62'], ['indian general election , 1991', '10th lok sabha', '120', '37', '20.11', '8.75'], ['indian general election , 1996', '11th lok sabha', '161', '41', '20.29', '0.18'], ['indian general election , 1998', '12th lok sabha', '183', '21', '25.59 %', '5.30'], ['indian general election , 1999', '13th lok sabha', '189', '6', '23.75', '1.84'], ['indian general election , 2004', '14th lok sabha', '144', '45', '22.16 %', '1.69']] |
fundraising for the 2008 united states presidential election | https://en.wikipedia.org/wiki/Fundraising_for_the_2008_United_States_presidential_election | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12030247-7.html.csv | superlative | the largest amount of money raised in the third quarter by a candidate for the 2008 united states presidential election was 11624255 . | {'scope': 'all', 'col_superlative': '2', 'row_superlative': '1', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': 'n/a', 'subset': None} | {'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'money raised , 3q'], 'result': '11624255', 'ind': 0, 'tostr': 'max { all_rows ; money raised , 3q }', 'tointer': 'the maximum money raised , 3q record of all rows is 11624255 .'}, '11624255'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; money raised , 3q } ; 11624255 } = true', 'tointer': 'the maximum money raised , 3q record of all rows is 11624255 .'} | eq { max { all_rows ; money raised , 3q } ; 11624255 } = true | the maximum money raised , 3q record of all rows is 11624255 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'max_0': 0, 'all_rows_3': 3, 'money raised , 3q_4': 4, '11624255_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'max_0': 'max', 'all_rows_3': 'all_rows', 'money raised , 3q_4': 'money raised , 3q', '11624255_5': '11624255'} | {'eq_1': [2], 'result_2': [], 'max_0': [1], 'all_rows_3': [0], 'money raised , 3q_4': [0], '11624255_5': [1]} | ['candidate', 'money raised , 3q', 'loans received , 3q', 'money spent , 3q', 'total receipts', 'cash on hand', 'total debt', 'after debt'] | [['rudy giuliani', '11624255', '-', '13300649', '47253520', '16649825', '169256', '16480569'], ['mitt romney', '9896719', '8500000', '21301755', '62829068', '9216517', '17350000', '- 8133483'], ['fred thompson', '9750820', '-', '5706366', '12828110', '7121744', '678432', '6443312'], ['ron paul', '5258455', '-', '2169644', '8268452', '5443667', '-', '5443667'], ['john mccain', '5734477', '-', '5470277', '32124785', '3488627', '1730691', '1757936'], ['mike huckabee', '1034486', '-', '819376', '2345797', '651300', '47810', '603490'], ['duncan hunter', '486356', '50000', '618117', '1890873', '132741', '50000', '82741'], ['tom tancredo', '767152', '-', '1209583', '3538244', '110079', '295603', '- 185524'], ['sam brownback', '925745', '-', '1278856', '4235333', '94653', '-', '94653']] |
1973 cleveland browns season | https://en.wikipedia.org/wiki/1973_Cleveland_Browns_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10651062-3.html.csv | aggregation | for games in december 1973 the cleveland browns average attendance was 67503 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '67503', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'attendance'], 'result': '67503', 'ind': 0, 'tostr': 'avg { all_rows ; attendance }'}, '67503'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; attendance } ; 67503 } = true', 'tointer': 'the average of the attendance record of all rows is 67503 .'} | round_eq { avg { all_rows ; attendance } ; 67503 } = true | the average of the attendance record of all rows is 67503 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '67503_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '67503_5': '67503'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '67503_5': [1]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 16 , 1973', 'baltimore colts', 'w 24 - 14', '74303'], ['2', 'september 23 , 1973', 'pittsburgh steelers', 'l 33 - 6', '49396'], ['3', 'september 30 , 1973', 'new york giants', 'w 12 - 10', '76065'], ['4', 'october 7 , 1973', 'cincinnati bengals', 'w 17 - 10', '70805'], ['5', 'october 15 , 1973', 'miami dolphins', 'l 17 - 9', '72070'], ['6', 'october 21 , 1973', 'houston oilers', 'w 42 - 13', '61146'], ['7', 'october 28 , 1973', 'san diego chargers', 't 16 - 16', '68244'], ['8', 'november 4 , 1973', 'minnesota vikings', 'l 26 - 3', '45590'], ['9', 'november 11 , 1973', 'houston oilers', 'w 23 - 13', '37230'], ['10', 'november 18 , 1973', 'oakland raiders', 'w 7 - 3', '47398'], ['11', 'november 25 , 1973', 'pittsburgh steelers', 'w 21 - 16', '67773'], ['12', 'december 2 , 1973', 'kansas city chiefs', 't 20 - 20', '70296'], ['13', 'december 9 , 1973', 'cincinnati bengals', 'l 34 - 17', '58266'], ['14', 'december 16 , 1973', 'los angeles rams', 'l 30 - 17', '73948']] |
list of colombian submissions for the academy award for best foreign language film | https://en.wikipedia.org/wiki/List_of_Colombian_submissions_for_the_Academy_Award_for_Best_Foreign_Language_Film | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22102732-1.html.csv | majority | of the colombian submissions for the academy award for best foreign language film , none of them were nominated . | {'scope': 'all', 'col': '5', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'not nominated', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'result', 'not nominated'], 'result': True, 'ind': 0, 'tointer': 'for the result records of all rows , all of them fuzzily match to not nominated .', 'tostr': 'all_eq { all_rows ; result ; not nominated } = true'} | all_eq { all_rows ; result ; not nominated } = true | for the result records of all rows , all of them fuzzily match to not nominated . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 'not nominated_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 'not nominated_4': 'not nominated'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 'not nominated_4': [0]} | ['year ( ceremony )', 'english title', 'spanish title', 'director', 'result'] | [['1980 ( 53rd )', 'the latin immigrant', 'el inmigrante latino', 'gustavo nieto roa', 'not nominated'], ['1984 ( 57th )', 'a man of principle', 'cóndores no entierran todos los días', 'francisco norden', 'not nominated'], ['1986 ( 59th )', 'a time to die', 'tiempo de morir', 'jorge alí triana', 'not nominated'], ['1991 ( 64th )', 'confessing to laura', 'confesión a laura', 'jaime osorio gómez', 'not nominated'], ['1994 ( 67th )', 'the strategy of the snail', 'la estrategia del caracol', 'sergio cabrera', 'not nominated'], ['1996 ( 69th )', 'oedipus mayor', 'edipo alcalde', 'jorge alí triana', 'not nominated'], ['1997 ( 70th )', 'the debt', 'la deuda', 'manuel jose alvarez & nicolas buenaventura', 'not nominated'], ['1998 ( 71st )', 'the rose seller', 'la vendedora de rosas', 'victor gaviria', 'not nominated'], ['1999 ( 72nd )', 'time out', 'golpe de estadio', 'sergio cabrera', 'not nominated'], ['2001 ( 74th )', 'our lady of the assassins', 'la virgen de los sicarios', 'barbet schroeder', 'not nominated'], ['2005 ( 78th )', 'wandering shadows', 'la sombra del caminante', 'ciro guerra', 'not nominated'], ['2006 ( 79th )', 'a ton of luck', 'soñar no cuesta nada', 'rodrigo triana', 'not nominated'], ['2007 ( 80th )', 'satanás', 'satanás', 'andi baiz', 'not nominated'], ['2008 ( 81st )', 'dog eat dog', 'perro come perro', 'carlos moreno', 'not nominated'], ['2009 ( 82nd )', 'the wind journeys', 'los viajes del viento', 'ciro guerra', 'not nominated'], ['2010 ( 83rd )', 'crab trap', 'el vuelco del cangrejo', 'oscar ruiz navia', 'not nominated'], ['2012 ( 85th )', 'the snitch cartel', 'el cartel de los sapos', 'carlos moreno', 'not nominated']] |
1979 miami dolphins season | https://en.wikipedia.org/wiki/1979_Miami_Dolphins_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18847736-2.html.csv | count | the 1979 dolphins only lost 6 games this season . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'loss', 'result': '6', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'loss'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to loss .', 'tostr': 'filter_eq { all_rows ; result ; loss }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; result ; loss } }', 'tointer': 'select the rows whose result record fuzzily matches to loss . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; result ; loss } } ; 6 } = true', 'tointer': 'select the rows whose result record fuzzily matches to loss . the number of such rows is 6 .'} | eq { count { filter_eq { all_rows ; result ; loss } } ; 6 } = true | select the rows whose result record fuzzily matches to loss . 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, 'result_5': 5, 'loss_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', 'result_5': 'result', 'loss_6': 'loss', '6_7': '6'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'result_5': [0], 'loss_6': [0], '6_7': [2]} | ['game', 'date', 'opponent', 'result', 'dolphins points', 'opponents', 'record', 'attendance'] | [['1', 'sept 2', 'buffalo bills', 'win', '9', '7', '1 - 0', '69441'], ['2', 'sept 9', 'seattle seahawks', 'win', '19', '10', '2 - 0', '56233'], ['3', 'sept 16', 'minnesota vikings', 'win', '27', '12', '3 - 0', '46187'], ['4', 'sept 23', 'chicago bears', 'win', '31', '16', '4 - 0', '66011'], ['5', 'sept 30', 'new york jets', 'loss', '27', '33', '4 - 1', '51496'], ['6', 'oct 8', 'oakland raiders', 'loss', '3', '13', '4 - 2', '52419'], ['7', 'oct 14', 'buffalo bills', 'win', '17', '7', '5 - 2', '45597'], ['8', 'oct 21', 'new england patriots', 'loss', '13', '28', '5 - 3', '61096'], ['9', 'oct 28', 'green bay packers', 'win', '27', '7', '6 - 3', '47741'], ['10', 'nov 5', 'houston oilers', 'loss', '6', '9', '6 - 4', '70273'], ['11', 'nov 11', 'baltimore colts', 'win', '19', '0', '7 - 4', '50193'], ['12', 'nov 18', 'cleveland browns', 'loss ( ot )', '24', '30', '7 - 5', '80374'], ['13', 'nov 25', 'baltimore colts', 'win', '28', '24', '8 - 5', '38016'], ['14', 'nov 29', 'new england patriots', 'win', '39', '24', '9 - 5', '69174'], ['15', 'dec 9', 'detroit lions', 'win', '28', '10', '10 - 5', '78087'], ['16', 'dec 15', 'new york jets', 'loss', '24', '27', '10 - 6', '49915']] |
utah jazz all - time roster | https://en.wikipedia.org/wiki/Utah_Jazz_all-time_roster | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11545282-18.html.csv | superlative | kirk snyder is the most recent player on the utah jazz all-time roster . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '5', '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', 'years for jazz'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; years for jazz }'}, 'player'], 'result': 'kirk snyder', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; years for jazz } ; player }'}, 'kirk snyder'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; years for jazz } ; player } ; kirk snyder } = true', 'tointer': 'select the row whose years for jazz record of all rows is maximum . the player record of this row is kirk snyder .'} | eq { hop { argmax { all_rows ; years for jazz } ; player } ; kirk snyder } = true | select the row whose years for jazz record of all rows is maximum . the player record of this row is kirk snyder . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'years for jazz_5': 5, 'player_6': 6, 'kirk snyder_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'years for jazz_5': 'years for jazz', 'player_6': 'player', 'kirk snyder_7': 'kirk snyder'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'years for jazz_5': [0], 'player_6': [1], 'kirk snyder_7': [2]} | ['player', 'no', 'nationality', 'position', 'years for jazz', 'school / club team'] | [['fred saunders', '12', 'united states', 'forward', '1977 - 78', 'syracuse'], ['danny schayes', '24', 'united states', 'forward - center', '1981 - 83', 'syracuse'], ['carey scurry', '22', 'united states', 'forward', '1985 - 88', 'long island'], ['robert smith', '5', 'united states', 'guard', '1979 - 80', 'unlv'], ['kirk snyder', '3', 'united states', 'guard', '2004 - 05', 'nevada'], ['felton spencer', '50', 'united states', 'center', '1993 - 96', 'louisville'], ['bud stallworth', '15', 'united states', 'guard - forward', '1974 - 77', 'kansas'], ['john starks', '9', 'united states', 'shooting guard', '2000 - 02', 'oklahoma state'], ['deshawn stevenson', '2', 'united states', 'shooting guard', '2000 - 04', 'washington union hs']] |
anaprof apertura 2008 | https://en.wikipedia.org/wiki/ANAPROF_Apertura_2008 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15682637-4.html.csv | ordinal | for anaprof apertura 2008 , when there are at least 5 games won , the 2nd highest number of goals scored was from chepo fc . | {'scope': 'subset', 'row': '2', 'col': '7', 'order': '2', 'col_other': '3', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'subset': {'col': '4', 'criterion': 'greater_than_eq', 'value': '5'}} | {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmax', 'args': [{'func': 'filter_greater_eq', 'args': ['all_rows', 'won ( pg )', '5'], 'result': None, 'ind': 0, 'tostr': 'filter_greater_eq { all_rows ; won ( pg ) ; 5 }', 'tointer': 'select the rows whose won ( pg ) record is greater than or equal to 5 .'}, 'goals scored ( gf )', '2'], 'result': None, 'ind': 1, 'tostr': 'nth_argmax { filter_greater_eq { all_rows ; won ( pg ) ; 5 } ; goals scored ( gf ) ; 2 }'}, 'played ( pj )'], 'result': '13', 'ind': 2, 'tostr': 'hop { nth_argmax { filter_greater_eq { all_rows ; won ( pg ) ; 5 } ; goals scored ( gf ) ; 2 } ; played ( pj ) }'}, '13'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmax { filter_greater_eq { all_rows ; won ( pg ) ; 5 } ; goals scored ( gf ) ; 2 } ; played ( pj ) } ; 13 } = true', 'tointer': 'select the rows whose won ( pg ) record is greater than or equal to 5 . select the row whose goals scored ( gf ) record of these rows is 2nd maximum . the played ( pj ) record of this row is 13 .'} | eq { hop { nth_argmax { filter_greater_eq { all_rows ; won ( pg ) ; 5 } ; goals scored ( gf ) ; 2 } ; played ( pj ) } ; 13 } = true | select the rows whose won ( pg ) record is greater than or equal to 5 . select the row whose goals scored ( gf ) record of these rows is 2nd maximum . the played ( pj ) record of this row is 13 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'num_hop_2': 2, 'nth_argmax_1': 1, 'filter_greater_eq_0': 0, 'all_rows_5': 5, 'won (pg)_6': 6, '5_7': 7, 'goals scored (gf)_8': 8, '2_9': 9, 'played (pj)_10': 10, '13_11': 11} | {'eq_3': 'eq', 'result_4': 'true', 'num_hop_2': 'num_hop', 'nth_argmax_1': 'nth_argmax', 'filter_greater_eq_0': 'filter_greater_eq', 'all_rows_5': 'all_rows', 'won (pg)_6': 'won ( pg )', '5_7': '5', 'goals scored (gf)_8': 'goals scored ( gf )', '2_9': '2', 'played (pj)_10': 'played ( pj )', '13_11': '13'} | {'eq_3': [4], 'result_4': [], 'num_hop_2': [3], 'nth_argmax_1': [2], 'filter_greater_eq_0': [1], 'all_rows_5': [0], 'won (pg)_6': [0], '5_7': [0], 'goals scored (gf)_8': [1], '2_9': [1], 'played (pj)_10': [2], '13_11': [3]} | ['place ( posición )', 'team ( equipo )', 'played ( pj )', 'won ( pg )', 'draw ( pe )', 'lost ( pp )', 'goals scored ( gf )', 'goals conceded ( gc )', '+ / - ( dif )', 'points ( pts )'] | [['1', 'tauro fc', '13', '7', '2', '4', '21', '18', '+ 3', '23'], ['2', 'chepo fc', '13', '5', '5', '3', '19', '12', '+ 7', '20'], ['3', 'sporting san miguelito', '13', '6', '2', '5', '18', '16', '+ 2', '20'], ['4', 'árabe unido', '13', '6', '2', '5', '15', '14', '+ 1', '20'], ['5', 'alianza', '13', '3', '1', '9', '25', '27', '- 2', '10']] |
mark mcnulty | https://en.wikipedia.org/wiki/Mark_McNulty | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1601826-4.html.csv | count | three of the tournaments were played in 2004 . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': '2004', 'result': '3', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '2004'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to 2004 .', 'tostr': 'filter_eq { all_rows ; date ; 2004 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; date ; 2004 } }', 'tointer': 'select the rows whose date record fuzzily matches to 2004 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; date ; 2004 } } ; 3 } = true', 'tointer': 'select the rows whose date record fuzzily matches to 2004 . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; date ; 2004 } } ; 3 } = true | select the rows whose date record fuzzily matches to 2004 . 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, 'date_5': 5, '2004_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', 'date_5': 'date', '2004_6': '2004', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'date_5': [0], '2004_6': [0], '3_7': [2]} | ['date', 'tournament', 'winning score', 'margin of victory', 'runner ( s ) - up'] | [['22 feb 2004', 'outback steakhouse pro - am', '13 ( 67 + 65 + 68 = 200 )', '1 stroke', 'larry nelson'], ['17 oct 2004', 'sbc championship', '18 ( 67 + 63 + 65 = 195 )', '8 strokes', 'gary mccord'], ['24 oct 2004', 'charles schwab cup championship', '11 ( 69 + 74 + 68 + 66 = 177 )', '1 stroke', 'tom kite'], ['26 jun 2005', 'bank of america championship', '12 ( 67 + 69 + 68 = 204 )', 'playoff', 'don pooley , tom purtzer'], ['16 oct 2005', 'administaff small business classic', '16 ( 66 + 68 + 66 = 200 )', '1 stroke', 'gil morgan'], ['19 aug 2007', 'jeld - wen tradition', '16 ( 66 + 68 + 70 + 68 = 272 )', '5 strokes', 'david edwards'], ['31 may 2009', 'principal charity classic', '10 ( 68 + 69 + 66 = 203 )', 'playoff', 'fred funk , nick price'], ['24 apr 2011', 'liberty mutual legends of golf ( with david eger )', '27 ( 64 + 64 + 61 = 189 )', 'playoff', 'scott hoch & kenny perry']] |
andrei tarkovsky filmography | https://en.wikipedia.org/wiki/Andrei_Tarkovsky_filmography | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15044621-3.html.csv | comparative | the andrei tarkovsky film ' sergey lazo ' was longer in running time than the film ' sour grape ' . | {'row_1': '3', 'row_2': '5', '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', 'english title', 'sergey lazo'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose english title record fuzzily matches to sergey lazo .', 'tostr': 'filter_eq { all_rows ; english title ; sergey lazo }'}, 'length'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; english title ; sergey lazo } ; length }', 'tointer': 'select the rows whose english title record fuzzily matches to sergey lazo . take the length record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'english title', 'sour grape'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose english title record fuzzily matches to sour grape .', 'tostr': 'filter_eq { all_rows ; english title ; sour grape }'}, 'length'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; english title ; sour grape } ; length }', 'tointer': 'select the rows whose english title record fuzzily matches to sour grape . take the length record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; english title ; sergey lazo } ; length } ; hop { filter_eq { all_rows ; english title ; sour grape } ; length } } = true', 'tointer': 'select the rows whose english title record fuzzily matches to sergey lazo . take the length record of this row . select the rows whose english title record fuzzily matches to sour grape . take the length record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; english title ; sergey lazo } ; length } ; hop { filter_eq { all_rows ; english title ; sour grape } ; length } } = true | select the rows whose english title record fuzzily matches to sergey lazo . take the length record of this row . select the rows whose english title record fuzzily matches to sour grape . take the length 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, 'english title_7': 7, 'sergey lazo_8': 8, 'length_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'english title_11': 11, 'sour grape_12': 12, 'length_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', 'english title_7': 'english title', 'sergey lazo_8': 'sergey lazo', 'length_9': 'length', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'english title_11': 'english title', 'sour grape_12': 'sour grape', 'length_13': 'length'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'english title_7': [0], 'sergey lazo_8': [0], 'length_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'english title_11': [1], 'sour grape_12': [1], 'length_13': [3]} | ['year', 'english title', 'original title', 'country', 'length', 'participation as'] | [['1956', 'the killers', 'убийцы', 'soviet union', '19 min', 'actor'], ['1964', 'i am twenty', 'мне двадцать лет', 'soviet union', '189 min', 'actor'], ['1968', 'sergey lazo', 'сергей лазо', 'soviet union', '89 min', 'actor , film editor'], ['1968', 'one chance in one thousand', 'один шанс из тысячи', 'soviet union', '81 min', 'artistic director'], ['1974', 'sour grape', 'терпкий виноград', 'soviet union', '76 min', 'artistic advisor']] |
list of corporations by market capitalization | https://en.wikipedia.org/wiki/List_of_corporations_by_market_capitalization | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14094649-21.html.csv | aggregation | for corporations whose primary industry is healthcare , the average market value , in usd millions , is 151413.5 . | {'scope': 'subset', 'col': '5', 'type': 'average', 'result': '151413.5', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'health care'}} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'primary industry', 'health care'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; primary industry ; health care }', 'tointer': 'select the rows whose primary industry record fuzzily matches to health care .'}, 'market value ( usd million )'], 'result': '151413.5', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; primary industry ; health care } ; market value ( usd million ) }'}, '151413.5'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; primary industry ; health care } ; market value ( usd million ) } ; 151413.5 } = true', 'tointer': 'select the rows whose primary industry record fuzzily matches to health care . the average of the market value ( usd million ) record of these rows is 151413.5 .'} | round_eq { avg { filter_eq { all_rows ; primary industry ; health care } ; market value ( usd million ) } ; 151413.5 } = true | select the rows whose primary industry record fuzzily matches to health care . the average of the market value ( usd million ) record of these rows is 151413.5 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'primary industry_5': 5, 'health care_6': 6, 'market value ( usd million)_7': 7, '151413.5_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'primary industry_5': 'primary industry', 'health care_6': 'health care', 'market value ( usd million)_7': 'market value ( usd million )', '151413.5_8': '151413.5'} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'primary industry_5': [0], 'health care_6': [0], 'market value ( usd million)_7': [1], '151413.5_8': [2]} | ['rank', 'name', 'headquarters', 'primary industry', 'market value ( usd million )'] | [['1', 'microsoft', 'united states', 'software industry', '271854'], ['2', 'general electric', 'united states', 'conglomerate', '258871'], ['3', 'exxon mobil', 'united states', 'oil and gas', '172213'], ['4', 'royal dutch shell', 'the netherlands', 'oil and gas', '164157'], ['5', 'merck', 'united states', 'health care', '154753'], ['6', 'pfizer', 'united states', 'health care', '148074'], ['7', 'intel corporation', 'united states', 'computer hardware', '144060'], ['8', 'the coca - cola company', 'united states', 'beverage', '142164'], ['9', 'wal - mart', 'united states', 'retail', '123062'], ['10', 'ibm', 'united states', 'software industry , computer hardware', '121184']] |
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/1-27987767-2.html.csv | superlative | the highest points margin of those with match points of 2-2 was 20 . | {'scope': 'subset', 'col_superlative': '4', 'row_superlative': '12', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': '2', 'subset': {'col': '2', 'criterion': 'equal', 'value': '2 - 2'}} | {'func': 'eq', 'args': [{'func': 'max', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'match points', '2 - 2'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; match points ; 2 - 2 }', 'tointer': 'select the rows whose match points record fuzzily matches to 2 - 2 .'}, 'points margin'], 'result': '20', 'ind': 1, 'tostr': 'max { filter_eq { all_rows ; match points ; 2 - 2 } ; points margin }', 'tointer': 'select the rows whose match points record fuzzily matches to 2 - 2 . the maximum points margin record of these rows is 20 .'}, '20'], 'result': True, 'ind': 2, 'tostr': 'eq { max { filter_eq { all_rows ; match points ; 2 - 2 } ; points margin } ; 20 } = true', 'tointer': 'select the rows whose match points record fuzzily matches to 2 - 2 . the maximum points margin record of these rows is 20 .'} | eq { max { filter_eq { all_rows ; match points ; 2 - 2 } ; points margin } ; 20 } = true | select the rows whose match points record fuzzily matches to 2 - 2 . the maximum points margin record of these rows is 20 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'max_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'match points_5': 5, '2 - 2_6': 6, 'points margin_7': 7, '20_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'max_1': 'max', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'match points_5': 'match points', '2 - 2_6': '2 - 2', 'points margin_7': 'points margin', '20_8': '20'} | {'eq_2': [3], 'result_3': [], 'max_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'match points_5': [0], '2 - 2_6': [0], 'points margin_7': [1], '20_8': [2]} | ['winners', 'match points', 'aggregate score', 'points margin', 'losers'] | [['bath', '4 - 0', '125 - 11', '114', "l'aquila"], ['montferrand', '4 - 0', '113 - 3', '110', 'leonessa'], ['saracens', '4 - 0', '127 - 18', '109', 'rugby roma'], ['castres olympique', '4 - 0', '128 - 24', '104', 'rovigo'], ['newcastle falcons', '4 - 0', '137 - 37', '100', 'valladolid rac'], ['nec harlequins', '4 - 0', '94 - 21', '73', 'el salvador'], ['grenoble', '4 - 0', '76 - 16', '60', 'gran parma'], ['glasgow', '4 - 0', '68 - 24', '44', 'montpellier'], ['colomiers', '4 - 0', '75 - 32', '43', 'petrarca padova'], ['narbonne', '4 - 0', '52 - 23', '29', 'rotherham'], ['pau', '4 - 0', '58 - 34', '24', 'overmach parma'], ['brive', '2 - 2', '61 - 41', '20', 'viadana'], ['london irish', '2 - 2', '62 - 54', '8', 'montauban']] |
brl v6 | https://en.wikipedia.org/wiki/BRL_V6 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15216339-1.html.csv | ordinal | sandor van es scored the second lowest amount of points of any of the drivers . | {'row': '3', 'col': '5', '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', 'points', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; points ; 2 }'}, 'driver'], 'result': 'sandor van es', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; points ; 2 } ; driver }'}, 'sandor van es'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; points ; 2 } ; driver } ; sandor van es } = true', 'tointer': 'select the row whose points record of all rows is 2nd minimum . the driver record of this row is sandor van es .'} | eq { hop { nth_argmin { all_rows ; points ; 2 } ; driver } ; sandor van es } = true | select the row whose points record of all rows is 2nd minimum . the driver record of this row is sandor van es . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'points_5': 5, '2_6': 6, 'driver_7': 7, 'sandor van es_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', 'points_5': 'points', '2_6': '2', 'driver_7': 'driver', 'sandor van es_8': 'sandor van es'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'points_5': [0], '2_6': [0], 'driver_7': [1], 'sandor van es_8': [2]} | ['season', 'driver', 'team', 'tyre', 'points'] | [['2004', 'donny crevels', 'weytech', 'h', '86'], ['2005', 'jeroen bleekemolen', 'us carworld racing', 'h', '262'], ['2006', 'sandor van es', 'collé racing', 'h', '203'], ['2007', 'donald molenaar', 'collé racing', 'h', '206'], ['2008', 'donald molenaar', 'collé racing', 'h', '230'], ['2009', 'donald molenaar', 'collé racing', 'h', '210']] |
1937 in brazilian football | https://en.wikipedia.org/wiki/1937_in_Brazilian_football | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15352382-1.html.csv | superlative | the corinthians team had the most points in the 1937 brazilian football season . | {'scope': 'all', 'col_superlative': '3', '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', 'points'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; points }'}, 'team'], 'result': 'corinthians', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; points } ; team }'}, 'corinthians'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; points } ; team } ; corinthians } = true', 'tointer': 'select the row whose points record of all rows is maximum . the team record of this row is corinthians .'} | eq { hop { argmax { all_rows ; points } ; team } ; corinthians } = true | select the row whose points record of all rows is maximum . the team record of this row is corinthians . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'points_5': 5, 'team_6': 6, 'corinthians_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'points_5': 'points', 'team_6': 'team', 'corinthians_7': 'corinthians'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'points_5': [0], 'team_6': [1], 'corinthians_7': [2]} | ['position', 'team', 'points', 'played', 'drawn', 'lost', 'against', 'difference'] | [['1', 'corinthians', '22', '14', '2', '2', '14', '19'], ['2', 'palestra itã ¡ lia - sp', '21', '14', '1', '3', '12', '23'], ['3', 'portuguesa santista', '19', '14', '3', '3', '18', '9'], ['4', 'estudantes paulista', '15', '14', '1', '6', '22', '11'], ['5', 'santos', '14', '14', '4', '5', '20', '7'], ['6', 'juventus', '11', '14', '3', '7', '28', '- 5']] |
bwf super series masters finals | https://en.wikipedia.org/wiki/BWF_Super_Series_Masters_Finals | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-20361783-1.html.csv | count | five bwf super series masters finals were played between 2008 through 2012 . | {'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', 'year'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record is arbitrary .', 'tostr': 'filter_all { all_rows ; year }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; year } }', 'tointer': 'select the rows whose year record is arbitrary . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; year } } ; 5 } = true', 'tointer': 'select the rows whose year record is arbitrary . the number of such rows is 5 .'} | eq { count { filter_all { all_rows ; year } } ; 5 } = true | select the rows whose year 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, 'year_5': 5, '5_6': 6} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'year_5': 'year', '5_6': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'year_5': [0], '5_6': [2]} | ['year', 'mens singles', 'womens singles', 'mens doubles', 'womens doubles', 'mixed doubles'] | [['2012', 'chen long', 'li xuerui', 'mathias boe carsten mogensen', 'wang xiaoli yu yang', 'joachim fischer nielsen christinna pedersen'], ['2011', 'lin dan', 'wang yihan', 'mathias boe carsten mogensen', 'wang xiaoli yu yang', 'zhang nan zhao yunlei'], ['2010', 'lee chong wei', 'wang shixian', 'mathias boe carsten mogensen', 'wang xiaoli yu yang', 'zhang nan zhao yunlei'], ['2009', 'lee chong wei', 'wong mew choo', 'jung jae - sung lee yong - dae', 'wong pei tty chin eei hui', 'joachim fischer nielsen christinna pedersen'], ['2008', 'lee chong wei', 'zhou mi', 'koo kien keat tan boon heong', 'wong pei tty chin eei hui', 'thomas laybourn kamilla rytter juhl']] |
2004 denver broncos season | https://en.wikipedia.org/wiki/2004_Denver_Broncos_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17993956-1.html.csv | aggregation | in the 2004 denver broncos season , a total of 1,135,404 fans attended games . | {'scope': 'all', 'col': '5', 'type': 'sum', 'result': '1135404', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'attendance'], 'result': '1135404', 'ind': 0, 'tostr': 'sum { all_rows ; attendance }'}, '1135404'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; attendance } ; 1135404 } = true', 'tointer': 'the sum of the attendance record of all rows is 1135404 .'} | round_eq { sum { all_rows ; attendance } ; 1135404 } = true | the sum of the attendance record of all rows is 1135404 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '1135404_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '1135404_5': '1135404'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '1135404_5': [1]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 12 , 2004', 'kansas city chiefs', 'w 34 - 24', '75939'], ['2', 'september 19 , 2004', 'jacksonville jaguars', 'l 7 - 6', '69127'], ['3', 'september 26 , 2004', 'san diego chargers', 'w 23 - 13', '74533'], ['4', 'october 3 , 2004', 'tampa bay buccaneers', 'w 16 - 13', '65341'], ['5', 'october 10 , 2004', 'carolina panthers', 'w 20 - 17', '75072'], ['6', 'october 17 , 2004', 'oakland raiders', 'w 31 - 3', '57293'], ['7', 'october 25 , 2004', 'cincinnati bengals', 'l 23 - 10', '65806'], ['8', 'october 31 , 2004', 'atlanta falcons', 'l 41 - 28', '75083'], ['9', 'november 7 , 2004', 'houston texans', 'w 31 - 13', '74292'], ['11', 'november 21 , 2004', 'new orleans saints', 'w 34 - 13', '64900'], ['12', 'november 28 , 2004', 'oakland raiders', 'l 25 - 24', '75936'], ['13', 'december 5 , 2004', 'san diego chargers', 'l 20 - 17', '65395'], ['14', 'december 12 , 2004', 'miami dolphins', 'w 20 - 17', '75027'], ['15', 'december 19 , 2004', 'kansas city chiefs', 'l 45 - 17', '77702'], ['16', 'december 25 , 2004', 'tennessee titans', 'w 37 - 16', '68809'], ['17', 'january 2 , 2005', 'indianapolis colts', 'w 33 - 14', '75149']] |
1962 oakland raiders season | https://en.wikipedia.org/wiki/1962_Oakland_Raiders_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12676700-1.html.csv | ordinal | the game that occurred on december 16 , 1962 had the second lowest attendance of all games during the 1962 oakland raiders season . | {'row': '14', 'col': '5', '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', 'attendance', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; attendance ; 2 }'}, 'date'], 'result': 'december 16 , 1962', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; attendance ; 2 } ; date }'}, 'december 16 , 1962'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; attendance ; 2 } ; date } ; december 16 , 1962 } = true', 'tointer': 'select the row whose attendance record of all rows is 2nd minimum . the date record of this row is december 16 , 1962 .'} | eq { hop { nth_argmin { all_rows ; attendance ; 2 } ; date } ; december 16 , 1962 } = true | select the row whose attendance record of all rows is 2nd minimum . the date record of this row is december 16 , 1962 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'attendance_5': 5, '2_6': 6, 'date_7': 7, 'december 16 , 1962_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', 'attendance_5': 'attendance', '2_6': '2', 'date_7': 'date', 'december 16 , 1962_8': 'december 16 , 1962'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'attendance_5': [0], '2_6': [0], 'date_7': [1], 'december 16 , 1962_8': [2]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 9 , 1962', 'new york titans', 'l 28 - 17', '12893'], ['2', 'september 23 , 1962', 'dallas texans', 'l 26 - 16', '12500'], ['3', 'september 30 , 1962', 'san diego chargers', 'l 42 - 33', '13000'], ['4', 'october 5 , 1962', 'denver broncos', 'l 44 - 7', '22452'], ['5', 'october 14 , 1962', 'denver broncos', 'l 23 - 6', '7000'], ['6', 'october 20 , 1962', 'buffalo bills', 'l 14 - 6', '21037'], ['7', 'october 26 , 1962', 'boston patriots', 'l 26 - 16', '12514'], ['8', 'november 4 , 1962', 'new york titans', 'l 31 - 21', '18247'], ['9', 'november 11 , 1962', 'houston oilers', 'l 28 - 20', '11000'], ['10', 'november 18 , 1962', 'buffalo bills', 'l 10 - 6', '12500'], ['11', 'november 25 , 1962', 'dallas texans', 'l 35 - 7', '13557'], ['12', 'december 2 , 1962', 'san diego chargers', 'l 31 - 21', '17874'], ['13', 'december 9 , 1962', 'houston oilers', 'l 32 - 17', '27400'], ['14', 'december 16 , 1962', 'boston patriots', 'w 20 - 0', '8000']] |
2010 - 11 slovak superliga | https://en.wikipedia.org/wiki/2010%E2%80%9311_Slovak_Superliga | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27683516-3.html.csv | count | four of the outgoing managers left because they were sacked . | {'scope': 'all', 'criterion': 'equal', 'value': 'sacked', 'result': '4', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'manner of departure', 'sacked'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose manner of departure record fuzzily matches to sacked .', 'tostr': 'filter_eq { all_rows ; manner of departure ; sacked }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; manner of departure ; sacked } }', 'tointer': 'select the rows whose manner of departure record fuzzily matches to sacked . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; manner of departure ; sacked } } ; 4 } = true', 'tointer': 'select the rows whose manner of departure record fuzzily matches to sacked . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; manner of departure ; sacked } } ; 4 } = true | select the rows whose manner of departure record fuzzily matches to sacked . 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, 'manner of departure_5': 5, 'sacked_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', 'manner of departure_5': 'manner of departure', 'sacked_6': 'sacked', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'manner of departure_5': [0], 'sacked_6': [0], '4_7': [2]} | ['team', 'outgoing manager', 'manner of departure', 'date of vacancy', 'table', 'incoming manager', 'date of appointment'] | [['tatran prešov', 'roman pivarník', 'sacked', '22 august 2010', 'pre - season', 'ladislav pecko', '23 august 2010'], ['mfk košice', 'žarko djurović', 'mutual agreement', '28 september 2010', 'pre - season', 'štefan tarkovič', '28 september 2010'], ['mfk ružomberok', 'ladislav jurkemik', 'mutual agreement', '10 october 2010', 'pre - season', 'goran milojević', '11 octobert 2010'], ['slovan bratislava', 'jozef jankech', 'mutual agreement', '13 october 2010', 'pre - season', 'karel jarolím', '13 octobert 2010'], ['dukla banská bystrica', 'karol marko', 'mutual agreement', '30 october 2010', 'pre - season', 'štefan zaťko', '8 november 2010'], ['fc nitra', 'ivan galád', 'sacked', '24 november 2010', 'pre - season', 'ivan vrabec', '21 december 2010'], ['fc nitra', 'ivan vrabec', 'sacked', '13 march 2011', 'pre - season', 'cyril stachura', '14 march 2011'], ['spartak trnava', 'dušan radolský', 'sacked', '19 march 2011', 'pre - season', 'peter zelenský', '22 march 2011']] |
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 | superlative | tlba 2 ca 3 cu 4 o 11 is the high - temperature superconductivity compound that has the highest number of cu - o planes in unit cell . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '8', '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', 'no of cu - o planes in unit cell'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; no of cu - o planes in unit cell }'}, 'formula'], 'result': 'tlba 2 ca 3 cu 4 o 11', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; no of cu - o planes in unit cell } ; formula }'}, 'tlba 2 ca 3 cu 4 o 11'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; no of cu - o planes in unit cell } ; formula } ; tlba 2 ca 3 cu 4 o 11 } = true', 'tointer': 'select the row whose no of cu - o planes in unit cell record of all rows is maximum . the formula record of this row is tlba 2 ca 3 cu 4 o 11 .'} | eq { hop { argmax { all_rows ; no of cu - o planes in unit cell } ; formula } ; tlba 2 ca 3 cu 4 o 11 } = true | select the row whose no of cu - o planes in unit cell record of all rows is maximum . the formula record of this row is tlba 2 ca 3 cu 4 o 11 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'no of cu - o planes in unit cell_5': 5, 'formula_6': 6, 'tlba 2 ca 3 cu 4 o 11_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'no of cu - o planes in unit cell_5': 'no of cu - o planes in unit cell', 'formula_6': 'formula', 'tlba 2 ca 3 cu 4 o 11_7': 'tlba 2 ca 3 cu 4 o 11'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'no of cu - o planes in unit cell_5': [0], 'formula_6': [1], 'tlba 2 ca 3 cu 4 o 11_7': [2]} | ['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']] |
list of star wars : the clone wars episodes | https://en.wikipedia.org/wiki/List_of_Star_Wars%3A_The_Clone_Wars_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19229713-6.html.csv | count | for the star wars : the clone wars episodes , eight of the episodes were written by chris collins . | {'scope': 'all', 'criterion': 'equal', 'value': 'chris collins', 'result': '8', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'written by', 'chris collins'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose written by record fuzzily matches to chris collins .', 'tostr': 'filter_eq { all_rows ; written by ; chris collins }'}], 'result': '8', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; written by ; chris collins } }', 'tointer': 'select the rows whose written by record fuzzily matches to chris collins . the number of such rows is 8 .'}, '8'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; written by ; chris collins } } ; 8 } = true', 'tointer': 'select the rows whose written by record fuzzily matches to chris collins . the number of such rows is 8 .'} | eq { count { filter_eq { all_rows ; written by ; chris collins } } ; 8 } = true | select the rows whose written by record fuzzily matches to chris collins . the number of such rows is 8 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'written by_5': 5, 'chris collins_6': 6, '8_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'written by_5': 'written by', 'chris collins_6': 'chris collins', '8_7': '8'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'written by_5': [0], 'chris collins_6': [0], '8_7': [2]} | ['no', '-', 'title', 'directed by', 'written by', 'original air date', 'production code', 'us viewers ( million )'] | [['89', '1', 'revival', 'steward lee', 'chris collins', 'september 29 , 2012', '4.26', '1.94'], ['90', '2', 'a war on two fronts', 'dave filoni', 'chris collins', 'october 6 , 2012', '4.15', '1.71'], ['91', '3', 'front runners', 'steward lee', 'chris collins', 'october 13 , 2012', '4.16', '1.75'], ['92', '4', 'the soft war', 'kyle dunlevy', 'chris collins', 'october 20 , 2012', '4.17', '1.57'], ['93', '5', 'tipping points', 'bosco ng', 'chris collins', 'october 27 , 2012', '4.18', '1.42'], ['94', '6', 'the gathering', 'kyle dunlevy', 'christian taylor', 'november 3 , 2012', '4.22', '1.66'], ['95', '7', 'a test of strength', 'bosco ng', 'christian taylor', 'november 10 , 2012', '4.23', '1.74'], ['96', '8', 'bound for rescue', "brian kalin o'connell", 'christian taylor', 'november 17 , 2012', '4.24', '1.96'], ['97', '9', 'a necessary bond', 'danny keller', 'christian taylor', 'november 24 , 2012', '4.25', '1.39'], ['98', '10', 'secret weapons', 'danny keller', 'brent friedman', 'december 1 , 2012', '5.04', '1.46'], ['99', '11', 'a sunny day in the void', 'kyle dunlevy', 'brent friedman', 'december 8 , 2012', '5.05', '1.43'], ['100', '12', 'missing in action', 'steward lee', 'brent friedman', 'january 5 , 2013', '5.06', '1.74'], ['101', '13', 'point of no return', 'bosco ng', 'brent friedman', 'january 12 , 2013', '5.07', '1.47'], ['102', '14', 'eminence', 'kyle dunlevy', 'chris collins', 'january 19 , 2013', '5.01', '1.85'], ['103', '15', 'shades of reason', 'bosco ng', 'chris collins', 'january 26 , 2013', '5.02', '1.83'], ['104', '16', 'the lawless', "brian kalin o'connell", 'chris collins', 'february 2 , 2013', '5.03', '1.86'], ['105', '17', 'sabotage', "brian kalin o'connell", 'charles murray', 'february 9 , 2013', '5.08', '2.02'], ['106', '18', 'the jedi who knew too much', 'danny keller', 'charles murray', 'february 16 , 2013', '5.09', '1.64'], ['107', '19', 'to catch a jedi', 'kyle dunlevy', 'charles murray', 'february 23 , 2013', '5.10', '2.06']] |
list of the colbert report episodes ( 2010 ) | https://en.wikipedia.org/wiki/List_of_The_Colbert_Report_episodes_%282010%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25691838-11.html.csv | comparative | david frum appeared on the colbert report before david stern made an appearance . | {'row_1': '1', 'row_2': '5', 'col': '5', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'guest', 'david frum , katrina vanden heuvel'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose guest record fuzzily matches to david frum , katrina vanden heuvel .', 'tostr': 'filter_eq { all_rows ; guest ; david frum , katrina vanden heuvel }'}, 'original airdate'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; guest ; david frum , katrina vanden heuvel } ; original airdate }', 'tointer': 'select the rows whose guest record fuzzily matches to david frum , katrina vanden heuvel . take the original airdate record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'guest', 'jeffrey goldberg , david stern'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose guest record fuzzily matches to jeffrey goldberg , david stern .', 'tostr': 'filter_eq { all_rows ; guest ; jeffrey goldberg , david stern }'}, 'original airdate'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; guest ; jeffrey goldberg , david stern } ; original airdate }', 'tointer': 'select the rows whose guest record fuzzily matches to jeffrey goldberg , david stern . take the original airdate record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; guest ; david frum , katrina vanden heuvel } ; original airdate } ; hop { filter_eq { all_rows ; guest ; jeffrey goldberg , david stern } ; original airdate } } = true', 'tointer': 'select the rows whose guest record fuzzily matches to david frum , katrina vanden heuvel . take the original airdate record of this row . select the rows whose guest record fuzzily matches to jeffrey goldberg , david stern . take the original airdate record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; guest ; david frum , katrina vanden heuvel } ; original airdate } ; hop { filter_eq { all_rows ; guest ; jeffrey goldberg , david stern } ; original airdate } } = true | select the rows whose guest record fuzzily matches to david frum , katrina vanden heuvel . take the original airdate record of this row . select the rows whose guest record fuzzily matches to jeffrey goldberg , david stern . take the original airdate 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, 'guest_7': 7, 'david frum , katrina vanden heuvel_8': 8, 'original airdate_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'guest_11': 11, 'jeffrey goldberg , david stern_12': 12, 'original airdate_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', 'guest_7': 'guest', 'david frum , katrina vanden heuvel_8': 'david frum , katrina vanden heuvel', 'original airdate_9': 'original airdate', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'guest_11': 'guest', 'jeffrey goldberg , david stern_12': 'jeffrey goldberg , david stern', 'original airdate_13': 'original airdate'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'guest_7': [0], 'david frum , katrina vanden heuvel_8': [0], 'original airdate_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'guest_11': [1], 'jeffrey goldberg , david stern_12': [1], 'original airdate_13': [3]} | ['episode', 'the wãrd', 'guest', 'introductory phrase', 'original airdate', 'production code'] | [['791', 'none', 'david frum , katrina vanden heuvel', 'shaka brah ! this is the colbert report !', 'november 02', '6139'], ['794', 'nothingness', 'reza aslan', 'none', 'november 08', '6142'], ['795', 'none', 'abbe lowell , cee lo green', 'none', 'november 09', '6143'], ['796', 'none', 'beri fox , martha stewart', 'none', 'november 10', '6144'], ['798', 'none', 'jeffrey goldberg , david stern', 'none', 'november 15', '6146'], ['801', 'none', 'salvatore giunta', 'none', 'november 18', '6149']] |
1957 ohio state buckeyes football team | https://en.wikipedia.org/wiki/1957_Ohio_State_Buckeyes_football_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17998617-1.html.csv | superlative | the ohio state buckeyes football team scored the most points in their october 19th game against indiana . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '4', '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', 'result'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; result }'}, 'date'], 'result': 'october 19', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; result } ; date }'}, 'october 19'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; result } ; date } ; october 19 } = true', 'tointer': 'select the row whose result record of all rows is maximum . the date record of this row is october 19 .'} | eq { hop { argmax { all_rows ; result } ; date } ; october 19 } = true | select the row whose result record of all rows is maximum . the date record of this row is october 19 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'result_5': 5, 'date_6': 6, 'october 19_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'result_5': 'result', 'date_6': 'date', 'october 19_7': 'october 19'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'result_5': [0], 'date_6': [1], 'october 19_7': [2]} | ['date', 'opponent', 'site', 'result', 'attendance'] | [['september 28', 'tcu', 'ohio stadium columbus , oh', 'l14 - 18', '81784'], ['october 5', 'washington', 'husky stadium seattle , wa', 'w35 - 7', '37500'], ['october 12', 'illinois', 'ohio stadium columbus , oh', 'w21 - 7', '82239'], ['october 19', 'indiana', 'ohio stadium columbus , oh', 'w56 - 0', '78348'], ['october 26', 'wisconsin', 'camp randall stadium madison , wi', 'w16 - 13', '51051'], ['november 2', 'northwestern', 'ohio stadium columbus , oh', 'w47 - 6', '79635'], ['november 9', 'purdue', 'ohio stadium columbus , oh', 'w20 - 7', '79177'], ['november 16', '5 iowa', 'ohio stadium columbus , oh', 'w17 - 13', '82935'], ['november 23', '19 michigan', 'michigan stadium ann arbor , mi', 'w31 - 14', '101001'], ['january 1', 'oregon', 'rose bowl pasadena , ca ( rose bowl )', 'w10 - 7', '98202']] |
1996 - 97 toronto raptors season | https://en.wikipedia.org/wiki/1996%E2%80%9397_Toronto_Raptors_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-13557843-8.html.csv | majority | when the toronto raptors played in the skydome , damon stoudamire usually scored the high points for the game . | {'scope': 'subset', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'damon stoudamire', 'subset': {'col': '8', 'criterion': 'fuzzily_match', 'value': 'skydome'}} | {'func': 'most_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location attendance', 'skydome'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; location attendance ; skydome }', 'tointer': 'select the rows whose location attendance record fuzzily matches to skydome .'}, 'high points', 'damon stoudamire'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose location attendance record fuzzily matches to skydome . for the high points records of these rows , most of them fuzzily match to damon stoudamire .', 'tostr': 'most_eq { filter_eq { all_rows ; location attendance ; skydome } ; high points ; damon stoudamire } = true'} | most_eq { filter_eq { all_rows ; location attendance ; skydome } ; high points ; damon stoudamire } = true | select the rows whose location attendance record fuzzily matches to skydome . for the high points records of these rows , most of them fuzzily match to damon stoudamire . | 2 | 2 | {'most_str_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'location attendance_4': 4, 'skydome_5': 5, 'high points_6': 6, 'damon stoudamire_7': 7} | {'most_str_eq_1': 'most_str_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'location attendance_4': 'location attendance', 'skydome_5': 'skydome', 'high points_6': 'high points', 'damon stoudamire_7': 'damon stoudamire'} | {'most_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'location attendance_4': [0], 'skydome_5': [0], 'high points_6': [1], 'damon stoudamire_7': [1]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['74', 'april 2', 'philadelphia', 'w 112 - 90 ( ot )', 'doug christie ( 29 )', 'doug christie ( 15 )', 'damon stoudamire ( 15 )', 'corestates center 13769', '27 - 47'], ['75', 'april 5', 'miami', 'l 84 - 98 ( ot )', 'damon stoudamire ( 25 )', 'marcus camby , clifford rozier ( 6 )', 'damon stoudamire ( 7 )', 'miami arena 15200', '27 - 48'], ['76', 'april 8', 'washington', 'w 100 - 94 ( ot )', 'damon stoudamire ( 29 )', 'clifford rozier ( 10 )', 'damon stoudamire ( 13 )', 'skydome 17159', '28 - 48'], ['77', 'april 10', 'orlando', 'l 69 - 105 ( ot )', 'sharone wright ( 17 )', 'popeye jones ( 12 )', 'damon stoudamire ( 5 )', 'skydome 20280', '28 - 49'], ['78', 'april 12', 'indiana', 'l 89 - 100 ( ot )', 'damon stoudamire ( 22 )', 'popeye jones , clifford rozier ( 11 )', 'damon stoudamire ( 11 )', 'skydome 21832', '28 - 50'], ['79', 'april 14', 'chicago', 'l 100 - 117 ( ot )', 'damon stoudamire ( 29 )', 'carlos rogers ( 12 )', 'damon stoudamire ( 12 )', 'united center 23896', '28 - 51'], ['80', 'april 15', 'milwaukee', 'l 85 - 92 ( ot )', 'reggie slater ( 19 )', 'clifford rozier ( 13 )', 'damon stoudamire ( 11 )', 'bradley center 14652', '28 - 52'], ['81', 'april 18', 'charlotte', 'w 108 - 100 ( ot )', 'damon stoudamire ( 28 )', 'marcus camby , popeye jones ( 8 )', 'damon stoudamire ( 9 )', 'charlotte coliseum 24042', '29 - 52']] |
2008 - 09 philadelphia flyers season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Philadelphia_Flyers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17511295-3.html.csv | superlative | the game played on october 11 drew the highest crowd attendance in the 2008 - 09 philadelphia flyers season . | {'scope': 'all', 'col_superlative': '6', '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', 'attendance'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; attendance }'}, 'date'], 'result': 'october 11', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; attendance } ; date }'}, 'october 11'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; date } ; october 11 } = true', 'tointer': 'select the row whose attendance record of all rows is maximum . the date record of this row is october 11 .'} | eq { hop { argmax { all_rows ; attendance } ; date } ; october 11 } = true | select the row whose attendance record of all rows is maximum . the date record of this row is october 11 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, 'date_6': 6, 'october 11_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', 'date_6': 'date', 'october 11_7': 'october 11'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], 'date_6': [1], 'october 11_7': [2]} | ['date', 'visitor', 'score', 'home', 'decision', 'attendance', 'record'] | [['october 11', 'ny rangers', '4 - 3', 'philadelphia', 'biron', '19623', '0 - 1 - 0'], ['october 13', 'montreal', '5 - 3', 'philadelphia', 'biron', '19323', '0 - 2 - 0'], ['october 14', 'philadelphia', '2 - 3', 'pittsburgh', 'niittymaki', '16965', '0 - 2 - 1'], ['october 16', 'philadelphia', '2 - 5', 'colorado', 'biron', '18007', '0 - 3 - 1'], ['october 18', 'philadelphia', '4 - 5', 'san jose', 'niittymaki', '17496', '0 - 3 - 2'], ['october 22', 'san jose', '7 - 6', 'philadelphia', 'biron', '19072', '0 - 3 - 3'], ['october 24', 'philadelphia', '6 - 3', 'new jersey', 'biron', '15529', '1 - 3 - 3'], ['october 25', 'new jersey', '2 - 3', 'philadelphia', 'biron', '19611', '2 - 3 - 3'], ['october 28', 'philadelphia', '7 - 0', 'atlanta', 'niittymaki', '13207', '3 - 3 - 3'], ['october 30', 'ny islanders', '2 - 3', 'philadelphia', 'biron', '18227', '4 - 3 - 3']] |
tokushima vortis | https://en.wikipedia.org/wiki/Tokushima_Vortis | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1276456-1.html.csv | ordinal | the 2005 season had the second highest attendance of all seasons . | {'row': '1', 'col': '5', 'order': '2', 'col_other': '1', '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', 'attendance / g', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; attendance / g ; 2 }'}, 'season'], 'result': '2005', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; attendance / g ; 2 } ; season }'}, '2005'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; attendance / g ; 2 } ; season } ; 2005 } = true', 'tointer': 'select the row whose attendance / g record of all rows is 2nd maximum . the season record of this row is 2005 .'} | eq { hop { nth_argmax { all_rows ; attendance / g ; 2 } ; season } ; 2005 } = true | select the row whose attendance / g record of all rows is 2nd maximum . the season record of this row is 2005 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'attendance / g_5': 5, '2_6': 6, 'season_7': 7, '2005_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'attendance / g_5': 'attendance / g', '2_6': '2', 'season_7': 'season', '2005_8': '2005'} | {'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'attendance / g_5': [0], '2_6': [0], 'season_7': [1], '2005_8': [2]} | ['season', 'div', 'tms', 'pos', 'attendance / g', 'j league cup', "emperor 's cup"] | [['2005', 'j2', '12', '9', '4366', '-', '4th round'], ['2006', 'j2', '13', '13', '3477', '-', '4th round'], ['2007', 'j2', '13', '13', '3289', '-', '4th round'], ['2008', 'j2', '15', '15', '3862', '-', '3rd round'], ['2009', 'j2', '18', '9', '4073', '-', '2nd round'], ['2010', 'j2', '19', '8', '4614', '-', '3rd round']] |
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/2-18974097-1.html.csv | ordinal | southwestern hanover was the 2nd largest school in indiana high school athletics conference . | {'row': '6', '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', 'size', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; size ; 2 }'}, 'school'], 'result': 'southwestern hanover', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; size ; 2 } ; school }'}, 'southwestern hanover'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; size ; 2 } ; school } ; southwestern hanover } = true', 'tointer': 'select the row whose size record of all rows is 2nd maximum . the school record of this row is southwestern hanover .'} | eq { hop { nth_argmax { all_rows ; size ; 2 } ; school } ; southwestern hanover } = true | select the row whose size record of all rows is 2nd maximum . the school record of this row is southwestern hanover . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'size_5': 5, '2_6': 6, 'school_7': 7, 'southwestern hanover_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', 'size_5': 'size', '2_6': '2', 'school_7': 'school', 'southwestern hanover_8': 'southwestern hanover'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'size_5': [0], '2_6': [0], 'school_7': [1], 'southwestern hanover_8': [2]} | ['school', 'location', 'mascot', 'size', 'ihsaa class', 'county'] | [['jac - cen - del', 'osgood , indiana', 'eagles', '279', 'a', '69 ripley'], ['milan', 'milan', 'indians', '408', 'aa', '69 ripley'], ['rising sun', 'rising sun', 'shiners', '243', 'a', '58 ohio'], ['madison shawe', 'madison', 'hilltoppers', '112', 'a', '39 jefferson'], ['south ripley', 'versailles', 'raiders', '375', 'aa', '69 ripley'], ['southwestern hanover', 'hanover', 'rebels', '411', 'aa', '39 jefferson'], ['switzerland county', 'vevay', 'pacers', '432', 'aa', '78 switzerland']] |
list of formula one driver records | https://en.wikipedia.org/wiki/List_of_Formula_One_driver_records | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-13599687-53.html.csv | superlative | rubens barrichello has the most entries out of all the drivers . | {'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', 'entries'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; entries }'}, 'driver'], 'result': 'rubens barrichello', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; entries } ; driver }'}, 'rubens barrichello'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; entries } ; driver } ; rubens barrichello } = true', 'tointer': 'select the row whose entries record of all rows is maximum . the driver record of this row is rubens barrichello .'} | eq { hop { argmax { all_rows ; entries } ; driver } ; rubens barrichello } = true | select the row whose entries record of all rows is maximum . the driver record of this row is rubens barrichello . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'entries_5': 5, 'driver_6': 6, 'rubens barrichello_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'entries_5': 'entries', 'driver_6': 'driver', 'rubens barrichello_7': 'rubens barrichello'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'entries_5': [0], 'driver_6': [1], 'rubens barrichello_7': [2]} | ['', 'driver', 'seasons', 'entries', '3rd places', 'percentage'] | [['1', 'rubens barrichello', '1993 - 2011', '326', '28', '8.58 %'], ['1', 'kimi räikkönen', '2001 - 2013', '194', '28', '14.43 %'], ['3', 'fernando alonso', '2001 , 2003 - 2013', '215', '26', '12.09 %'], ['4', 'david coulthard', '1994 - 2008', '247', '23', '9.31 %'], ['5', 'gerhard berger', '1984 - 1997', '210', '21', '10.00 %'], ['5', 'michael schumacher', '1991 - 2006 , 2010 - 2012', '308', '21', '6.81 %'], ['7', 'carlos reutemann', '1972 - 1982', '146', '20', '13.69 %'], ['7', 'alain prost', '1980 - 1991 , 1993', '202', '20', '9.90 %'], ['9', 'jenson button', '2000 - 2013', '247', '19', '7.69 %']] |
list of whose line is it anyway ? uk episodes | https://en.wikipedia.org/wiki/List_of_Whose_Line_Is_It_Anyway%3F_UK_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14934885-8.html.csv | superlative | ryan stiles is the fourth performer of the earliest episode of series 7 of the uk edition of whose line is it anyway ? . | {'scope': 'all', 'col_superlative': '1', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '6', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'date'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; date }'}, 'performer 4'], 'result': 'ryan stiles', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; date } ; performer 4 }'}, 'ryan stiles'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; date } ; performer 4 } ; ryan stiles } = true', 'tointer': 'select the row whose date record of all rows is minimum . the performer 4 record of this row is ryan stiles .'} | eq { hop { argmin { all_rows ; date } ; performer 4 } ; ryan stiles } = true | select the row whose date record of all rows is minimum . the performer 4 record of this row is ryan stiles . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'date_5': 5, 'performer 4_6': 6, 'ryan stiles_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'date_5': 'date', 'performer 4_6': 'performer 4', 'ryan stiles_7': 'ryan stiles'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'date_5': [0], 'performer 4_6': [1], 'ryan stiles_7': [2]} | ['date', 'episode', 'performer 1', 'performer 2', 'performer 3', 'performer 4'] | [['28 july 1995', '1', 'greg proops', 'colin mochrie', 'niall ashdown', 'ryan stiles'], ['4 august 1995', '2', 'greg proops', 'mike mcshane', 'ryan stiles', 'tony slattery'], ['11 august 1995', '3', 'stephen frost', 'colin mochrie', 'ryan stiles', 'tony slattery'], ['18 august 1995', '4', 'colin mochrie', 'ryan stiles', 'caroline quentin', 'tony slattery'], ['25 august 1995', '5', 'greg proops', 'mike mcshane', 'ryan stiles', 'tony slattery'], ['1 september 1995', '6', 'mike mcshane', 'colin mochrie', 'ryan stiles', 'tony slattery'], ['8 september 1995', '7', 'stephen frost', 'eddie izzard', 'greg proops', 'ryan stiles'], ['15 september 1995', '8', 'stephen frost', 'josie lawrence', 'colin mochrie', 'ryan stiles'], ['22 september 1995', '9', 'josie lawrence', 'caroline quentin', 'colin mochrie', 'ryan stiles'], ['29 september 1995', '10', 'stephen frost', 'josie lawrence', 'colin mochrie', 'ryan stiles'], ['6 october 1995', '11', 'compilation 1', 'compilation 1', 'compilation 1', 'compilation 1'], ['13 october 1995', '12', 'compilation 2', 'compilation 2', 'compilation 2', 'compilation 2']] |
fuck them all | https://en.wikipedia.org/wiki/Fuck_Them_All | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14754771-1.html.csv | unique | only one version of the song was released after 2005 , which is the live version which came out in 2006 . | {'scope': 'all', 'row': '8', 'col': '5', 'col_other': '1', 'criterion': 'greater_than', 'value': '2005', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'year', '2005'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record is greater than 2005 .', 'tostr': 'filter_greater { all_rows ; year ; 2005 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; year ; 2005 } }', 'tointer': 'select the rows whose year record is greater than 2005 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'year', '2005'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record is greater than 2005 .', 'tostr': 'filter_greater { all_rows ; year ; 2005 }'}, 'version'], 'result': 'live version ( recorded in 2006 )', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; year ; 2005 } ; version }'}, 'live version ( recorded in 2006 )'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; year ; 2005 } ; version } ; live version ( recorded in 2006 ) }', 'tointer': 'the version record of this unqiue row is live version ( recorded in 2006 ) .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; year ; 2005 } } ; eq { hop { filter_greater { all_rows ; year ; 2005 } ; version } ; live version ( recorded in 2006 ) } } = true', 'tointer': 'select the rows whose year record is greater than 2005 . there is only one such row in the table . the version record of this unqiue row is live version ( recorded in 2006 ) .'} | and { only { filter_greater { all_rows ; year ; 2005 } } ; eq { hop { filter_greater { all_rows ; year ; 2005 } ; version } ; live version ( recorded in 2006 ) } } = true | select the rows whose year record is greater than 2005 . there is only one such row in the table . the version record of this unqiue row is live version ( recorded in 2006 ) . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'year_7': 7, '2005_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'version_9': 9, 'live version (recorded in 2006)_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'year_7': 'year', '2005_8': '2005', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'version_9': 'version', 'live version (recorded in 2006)_10': 'live version ( recorded in 2006 )'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'year_7': [0], '2005_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'version_9': [2], 'live version (recorded in 2006)_10': [3]} | ['version', 'length', 'album', 'remixed by', 'year'] | [['album / single version', '4:30', "avant que l'ombre", '-', '2005'], ['radio edit', '3:55', '-', '-', '2005'], ['instrumental', '4:32', '-', 'laurent boutonnat', '2005'], ["the martyr 's remix", '5:20', '-', 'y - front', '2005'], ['mother f dub mix', '7:50', '-', 'joachim garraud', '2005'], ['mother f vocal club mix', '8:30', '-', 'joachim garraud', '2005'], ['music video', '5:02', 'music videos iv', '-', '2005'], ['live version ( recorded in 2006 )', '6:42 ( audio ) 8:18 ( video )', "avant que l'ombre à bercy", '-', '2006']] |
strikeout | https://en.wikipedia.org/wiki/Strikeout | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-242813-2.html.csv | comparative | toad ramsey recorded more strikeouts than old hoss radbourn in baseball . | {'row_1': '2', 'row_2': '4', 'col': '2', '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', 'pitcher', 'toad ramsey'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose pitcher record fuzzily matches to toad ramsey .', 'tostr': 'filter_eq { all_rows ; pitcher ; toad ramsey }'}, 'strikeouts'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; pitcher ; toad ramsey } ; strikeouts }', 'tointer': 'select the rows whose pitcher record fuzzily matches to toad ramsey . take the strikeouts record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'pitcher', 'old hoss radbourn'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose pitcher record fuzzily matches to old hoss radbourn .', 'tostr': 'filter_eq { all_rows ; pitcher ; old hoss radbourn }'}, 'strikeouts'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; pitcher ; old hoss radbourn } ; strikeouts }', 'tointer': 'select the rows whose pitcher record fuzzily matches to old hoss radbourn . take the strikeouts record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; pitcher ; toad ramsey } ; strikeouts } ; hop { filter_eq { all_rows ; pitcher ; old hoss radbourn } ; strikeouts } } = true', 'tointer': 'select the rows whose pitcher record fuzzily matches to toad ramsey . take the strikeouts record of this row . select the rows whose pitcher record fuzzily matches to old hoss radbourn . take the strikeouts record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; pitcher ; toad ramsey } ; strikeouts } ; hop { filter_eq { all_rows ; pitcher ; old hoss radbourn } ; strikeouts } } = true | select the rows whose pitcher record fuzzily matches to toad ramsey . take the strikeouts record of this row . select the rows whose pitcher record fuzzily matches to old hoss radbourn . take the strikeouts 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, 'pitcher_7': 7, 'toad ramsey_8': 8, 'strikeouts_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'pitcher_11': 11, 'old hoss radbourn_12': 12, 'strikeouts_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', 'pitcher_7': 'pitcher', 'toad ramsey_8': 'toad ramsey', 'strikeouts_9': 'strikeouts', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'pitcher_11': 'pitcher', 'old hoss radbourn_12': 'old hoss radbourn', 'strikeouts_13': 'strikeouts'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'pitcher_7': [0], 'toad ramsey_8': [0], 'strikeouts_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'pitcher_11': [1], 'old hoss radbourn_12': [1], 'strikeouts_13': [3]} | ['pitcher', 'strikeouts', 'season', 'team', 'league', 'overall rank'] | [['matt kilroy', '513', '1886', 'baltimore orioles', 'aa', '1'], ['toad ramsey', '499', '1886', 'louisville colonels', 'aa', '2'], ['dupee shaw', '451', '1884', 'detroit wolverines / boston reds', 'nl / ua', '4'], ['old hoss radbourn', '441', '1884', 'providence grays', 'nl', '5'], ['charlie buffington', '417', '1884', 'boston beaneaters', 'nl', '6'], ['guy hecker', '385', '1884', 'louisville eclipse', 'aa', '7'], ['nolan ryan', '383', '1973', 'california angels', 'al', '8'], ['sandy koufax', '382', '1965', 'los angeles dodgers', 'nl', '9']] |
87th united states congress | https://en.wikipedia.org/wiki/87th_United_States_Congress | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1802522-4.html.csv | comparative | in the 87th united states congress , william f norrel died before walter m mumma . | {'row_1': '1', 'row_2': '2', 'col': '3', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'vacator', 'william f norrell ( d )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose vacator record fuzzily matches to william f norrell ( d ) .', 'tostr': 'filter_eq { all_rows ; vacator ; william f norrell ( d ) }'}, 'reason for change'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; vacator ; william f norrell ( d ) } ; reason for change }', 'tointer': 'select the rows whose vacator record fuzzily matches to william f norrell ( d ) . take the reason for change record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'vacator', 'walter m mumma ( r )'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose vacator record fuzzily matches to walter m mumma ( r ) .', 'tostr': 'filter_eq { all_rows ; vacator ; walter m mumma ( r ) }'}, 'reason for change'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; vacator ; walter m mumma ( r ) } ; reason for change }', 'tointer': 'select the rows whose vacator record fuzzily matches to walter m mumma ( r ) . take the reason for change record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; vacator ; william f norrell ( d ) } ; reason for change } ; hop { filter_eq { all_rows ; vacator ; walter m mumma ( r ) } ; reason for change } } = true', 'tointer': 'select the rows whose vacator record fuzzily matches to william f norrell ( d ) . take the reason for change record of this row . select the rows whose vacator record fuzzily matches to walter m mumma ( r ) . take the reason for change record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; vacator ; william f norrell ( d ) } ; reason for change } ; hop { filter_eq { all_rows ; vacator ; walter m mumma ( r ) } ; reason for change } } = true | select the rows whose vacator record fuzzily matches to william f norrell ( d ) . take the reason for change record of this row . select the rows whose vacator record fuzzily matches to walter m mumma ( r ) . take the reason for change 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, 'vacator_7': 7, 'william f norrell (d)_8': 8, 'reason for change_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'vacator_11': 11, 'walter m mumma (r)_12': 12, 'reason for change_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', 'vacator_7': 'vacator', 'william f norrell (d)_8': 'william f norrell ( d )', 'reason for change_9': 'reason for change', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'vacator_11': 'vacator', 'walter m mumma (r)_12': 'walter m mumma ( r )', 'reason for change_13': 'reason for change'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'vacator_7': [0], 'william f norrell (d)_8': [0], 'reason for change_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'vacator_11': [1], 'walter m mumma (r)_12': [1], 'reason for change_13': [3]} | ['district', 'vacator', 'reason for change', 'successor', 'date successor seated'] | [['arkansas 6th', 'william f norrell ( d )', 'died february 15 , 1961', 'catherine dorris norrell ( d )', 'april 18 , 1961'], ['pennsylvania 16th', 'walter m mumma ( r )', 'died february 25 , 1961', 'john c kunkel ( r )', 'may 16 , 1961'], ['tennessee 1st', 'b carroll reece ( r )', 'died march 19 , 1961', 'louise goff reece ( r )', 'may 16 , 1961'], ['louisiana 4th', 'overton brooks ( d )', 'died september 16 , 1961', 'joe waggonner ( d )', 'october 19 , 1961'], ['michigan 14th', 'louis c rabaut ( d )', 'died november 12 , 1961', 'harold m ryan ( d )', 'february 13 , 1962'], ['texas 4th', 'sam rayburn ( d )', 'died november 16 , 1961', 'ray roberts ( d )', 'january 30 , 1962'], ['texas 13th', 'frank n ikard ( d )', 'resigned december 15 , 1961', 'graham b purcell , jr ( d )', 'january 27 , 1962'], ['south carolina 2nd', 'john j riley ( d )', 'died january 1 , 1962', 'corinne boyd riley ( d )', 'april 10 , 1962'], ['california 1st', 'clement w miller ( d )', 'died october 7 , 1962', 'vacant', 'not filled this term']] |
just legal | https://en.wikipedia.org/wiki/Just_Legal | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2828803-1.html.csv | unique | pilot was the only episode of " just legal " with more than 3000 viewers . | {'scope': 'all', 'row': '1', 'col': '7', 'col_other': '2', 'criterion': 'greater_than', 'value': '3.0', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'us viewers ( millions )', '3.0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose us viewers ( millions ) record is greater than 3.0 .', 'tostr': 'filter_greater { all_rows ; us viewers ( millions ) ; 3.0 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; us viewers ( millions ) ; 3.0 } }', 'tointer': 'select the rows whose us viewers ( millions ) record is greater than 3.0 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'us viewers ( millions )', '3.0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose us viewers ( millions ) record is greater than 3.0 .', 'tostr': 'filter_greater { all_rows ; us viewers ( millions ) ; 3.0 }'}, 'title'], 'result': 'pilot', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; us viewers ( millions ) ; 3.0 } ; title }'}, 'pilot'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; us viewers ( millions ) ; 3.0 } ; title } ; pilot }', 'tointer': 'the title record of this unqiue row is pilot .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; us viewers ( millions ) ; 3.0 } } ; eq { hop { filter_greater { all_rows ; us viewers ( millions ) ; 3.0 } ; title } ; pilot } } = true', 'tointer': 'select the rows whose us viewers ( millions ) record is greater than 3.0 . there is only one such row in the table . the title record of this unqiue row is pilot .'} | and { only { filter_greater { all_rows ; us viewers ( millions ) ; 3.0 } } ; eq { hop { filter_greater { all_rows ; us viewers ( millions ) ; 3.0 } ; title } ; pilot } } = true | select the rows whose us viewers ( millions ) record is greater than 3.0 . there is only one such row in the table . the title record of this unqiue row is pilot . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'us viewers (millions)_7': 7, '3.0_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'title_9': 9, 'pilot_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'us viewers (millions)_7': 'us viewers ( millions )', '3.0_8': '3.0', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'title_9': 'title', 'pilot_10': 'pilot'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'us viewers (millions)_7': [0], '3.0_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'title_9': [2], 'pilot_10': [3]} | ['series', 'title', 'written by', 'directed by', 'original air date', 'production code', 'us viewers ( millions )'] | [['1', 'pilot', 'jonathan shapiro', 'andrew davis', 'september 19 , 2005', '475279', '3.440'], ['2', 'the runner', 'jonathan shapiro', 'dwight little', 'september 26 , 2005', '2t7001', '2.960'], ['3', 'the limit', 'rob bragin', 'john badham', 'october 3 , 2005', '2t7002', '2.880'], ['4', 'the body in the trunk', "craig o'neil & jason tracy", 'tim matheson', 'august 13 , 2006', '2t7003', 'n / a'], ['5', 'the heater', 'nick thiel', 'dennis smith', 'august 20 , 2006', '2t7004', '1.590'], ['6', 'the rainmaker', 'rama laurie stagner', 'dwight little', 'august 27 , 2006', '2t7005', '1.120'], ['7', 'the code', 'alfredo barrios jr', 'oz scott', 'september 3 , 2006', '2t7006', '1.340']] |
united states house of representatives elections , 1918 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1918 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1346118-2.html.csv | count | out of the 6 districts in alabama reported in the united states house of representatives elections of 1918 , 6 representatives were democrats . | {'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '6', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'party'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose party record is arbitrary .', 'tostr': 'filter_all { all_rows ; party }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; party } }', 'tointer': 'select the rows whose party record is arbitrary . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; party } } ; 6 } = true', 'tointer': 'select the rows whose party record is arbitrary . the number of such rows is 6 .'} | eq { count { filter_all { all_rows ; party } } ; 6 } = true | select the rows whose party record is arbitrary . the number of such rows is 6 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'party_5': 5, '6_6': 6} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'party_5': 'party', '6_6': '6'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'party_5': [0], '6_6': [2]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['alabama 1', 'oscar lee gray', 'democratic', '1914', 'retired democratic hold', 'john mcduffie ( d ) unopposed'], ['alabama 2', 's hubert dent , jr', 'democratic', '1908', 're - elected', 's hubert dent , jr ( d ) unopposed'], ['alabama 3', 'henry b steagall', 'democratic', '1914', 're - elected', 'henry b steagall ( d ) unopposed'], ['alabama 5', 'j thomas heflin', 'democratic', '1904', 're - elected', 'j thomas heflin ( d ) unopposed'], ['alabama 6', 'william b oliver', 'democratic', '1914', 're - elected', 'william b oliver ( d ) unopposed'], ['alabama 8', 'edward b almon', 'democratic', '1914', 're - elected', 'edward b almon ( d ) unopposed']] |
frank kratovil | https://en.wikipedia.org/wiki/Frank_Kratovil | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16353840-1.html.csv | aggregation | the winner of the office received an average number of 76762 votes in frank kratovil 's elections . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '76762', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'votes'], 'result': '76762', 'ind': 0, 'tostr': 'avg { all_rows ; votes }'}, '76762'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; votes } ; 76762 } = true', 'tointer': 'the average of the votes record of all rows is 76762 .'} | round_eq { avg { all_rows ; votes } ; 76762 } = true | the average of the votes record of all rows is 76762 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'votes_4': 4, '76762_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'votes_4': 'votes', '76762_5': '76762'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'votes_4': [0], '76762_5': [1]} | ['year', 'office', 'election', 'subject', 'party', 'votes'] | [['2002', "queen anne 's county state 's attorney", 'general', 'frank kratovil', 'democratic', '9169'], ['2006', "queen anne 's county state 's attorney", 'general', 'frank kratovil', 'democratic', '13894'], ['2008', "us house , maryland 's 1st district", 'primary', 'frank kratovil', 'democratic', '28566'], ['2008', "us house , maryland 's 1st district", 'general', 'frank kratovil', 'democratic', '177065'], ['2010', "us house , maryland 's 1st district", 'general', 'andy harris', 'republican', '155118']] |
1929 in brazilian football | https://en.wikipedia.org/wiki/1929_in_Brazilian_football | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15372465-2.html.csv | aggregation | all of the brazilian football teams scored an average of 19.5 points . | {'scope': 'all', 'col': '3', 'type': 'average', 'result': '19.5', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'points'], 'result': '19.5', 'ind': 0, 'tostr': 'avg { all_rows ; points }'}, '19.5'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; points } ; 19.5 } = true', 'tointer': 'the average of the points record of all rows is 19.5 .'} | round_eq { avg { all_rows ; points } ; 19.5 } = true | the average of the points record of all rows is 19.5 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'points_4': 4, '19.5_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'points_4': 'points', '19.5_5': '19.5'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'points_4': [0], '19.5_5': [1]} | ['position', 'team', 'points', 'played', 'drawn', 'lost', 'against', 'difference'] | [['1', 'paulistano', '30', '19', '2', '3', '15', '38'], ['2', 'ponte preta', '26', '20', '2', '6', '36', '19'], ['3', 'sc internacional de são paulo', '23', '18', '5', '4', '23', '11'], ['4', 'independência', '23', '20', '5', '7', '37', '5'], ['5', 'hespanha', '22', '20', '6', '6', '35', '11'], ['6', 'atlético santista', '19', '19', '5', '7', '28', '6'], ['7', 'germnia', '18', '18', '2', '8', '45', '- 7'], ['8', 'portuguesa santista', '18', '21', '4', '10', '40', '- 3'], ['9', 'antártica', '17', '21', '7', '9', '47', '- 17'], ['10', 'aa são bento', '16', '19', '6', '8', '32', '- 12'], ['11', 'aa das palmeiras', '11', '17', '1', '11', '50', '- 22'], ['12', 'ca paulista', '11', '20', '1', '14', '58', '- 29']] |
54th united states congress | https://en.wikipedia.org/wiki/54th_United_States_Congress | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2417445-4.html.csv | unique | in the 54th united states congress , of the successors that took office in 1895 , the only time that the vacancy was due to a resignation was when the vacator was edwin j jordan . | {'scope': 'subset', 'row': '1', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'resigned', 'subset': {'col': '5', 'criterion': 'fuzzily_match', 'value': '1895'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date of successors taking office', '1895'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date of successors taking office ; 1895 }', 'tointer': 'select the rows whose date of successors taking office record fuzzily matches to 1895 .'}, 'reason for vacancy', 'resigned'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date of successors taking office record fuzzily matches to 1895 . among these rows , select the rows whose reason for vacancy record fuzzily matches to resigned .', 'tostr': 'filter_eq { filter_eq { all_rows ; date of successors taking office ; 1895 } ; reason for vacancy ; resigned }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; date of successors taking office ; 1895 } ; reason for vacancy ; resigned } }', 'tointer': 'select the rows whose date of successors taking office record fuzzily matches to 1895 . among these rows , select the rows whose reason for vacancy record fuzzily matches to resigned . 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', 'date of successors taking office', '1895'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date of successors taking office ; 1895 }', 'tointer': 'select the rows whose date of successors taking office record fuzzily matches to 1895 .'}, 'reason for vacancy', 'resigned'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date of successors taking office record fuzzily matches to 1895 . among these rows , select the rows whose reason for vacancy record fuzzily matches to resigned .', 'tostr': 'filter_eq { filter_eq { all_rows ; date of successors taking office ; 1895 } ; reason for vacancy ; resigned }'}, 'vacator'], 'result': 'edwin j jordan ( r )', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; date of successors taking office ; 1895 } ; reason for vacancy ; resigned } ; vacator }'}, 'edwin j jordan ( r )'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; date of successors taking office ; 1895 } ; reason for vacancy ; resigned } ; vacator } ; edwin j jordan ( r ) }', 'tointer': 'the vacator record of this unqiue row is edwin j jordan ( r ) .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; date of successors taking office ; 1895 } ; reason for vacancy ; resigned } } ; eq { hop { filter_eq { filter_eq { all_rows ; date of successors taking office ; 1895 } ; reason for vacancy ; resigned } ; vacator } ; edwin j jordan ( r ) } } = true', 'tointer': 'select the rows whose date of successors taking office record fuzzily matches to 1895 . among these rows , select the rows whose reason for vacancy record fuzzily matches to resigned . there is only one such row in the table . the vacator record of this unqiue row is edwin j jordan ( r ) .'} | and { only { filter_eq { filter_eq { all_rows ; date of successors taking office ; 1895 } ; reason for vacancy ; resigned } } ; eq { hop { filter_eq { filter_eq { all_rows ; date of successors taking office ; 1895 } ; reason for vacancy ; resigned } ; vacator } ; edwin j jordan ( r ) } } = true | select the rows whose date of successors taking office record fuzzily matches to 1895 . among these rows , select the rows whose reason for vacancy record fuzzily matches to resigned . there is only one such row in the table . the vacator record of this unqiue row is edwin j jordan ( r ) . | 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, 'date of successors taking office_8': 8, '1895_9': 9, 'reason for vacancy_10': 10, 'resigned_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'vacator_12': 12, 'edwin j jordan (r)_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', 'date of successors taking office_8': 'date of successors taking office', '1895_9': '1895', 'reason for vacancy_10': 'reason for vacancy', 'resigned_11': 'resigned', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'vacator_12': 'vacator', 'edwin j jordan (r)_13': 'edwin j jordan ( r )'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'date of successors taking office_8': [0], '1895_9': [0], 'reason for vacancy_10': [1], 'resigned_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'vacator_12': [3], 'edwin j jordan (r)_13': [4]} | ['district', 'vacator', 'reason for vacancy', 'successor', 'date of successors taking office'] | [['pennsylvania 15th', 'edwin j jordan ( r )', 'resigned march 4 , 1895', 'james h codding ( r )', 'november 5 , 1895'], ['utah at - large', 'new seat', 'state was admitted to the union', 'clarence e allen ( r )', 'january 4 , 1896'], ['massachusetts 6th', 'william cogswell ( r )', 'died may 22 , 1895', 'william h moody ( r )', 'november 5 , 1895'], ['illinois 18th', 'frederick remann ( r )', 'died july 14 , 1895', 'william f l hadley ( r )', 'december 2 , 1895'], ['utah territory al', 'frank j cannon ( r )', 'resigned january 4 , 1896', 'statehood achieved', 'statehood achieved'], ['texas 11th', 'william h crain ( d )', 'died february 10 , 1896', 'rudolph kleberg ( d )', 'april 7 , 1896'], ['georgia 3rd', 'charles f crisp ( d )', 'died october 23 , 1896', 'charles r crisp ( d )', 'december 19 , 1896']] |
anthony thomas ( american football ) | https://en.wikipedia.org/wiki/Anthony_Thomas_%28American_football%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14013061-2.html.csv | unique | anthony thomas had the only top 5 michigan wolverines runningback single season performance of the year 2000 . | {'scope': 'all', 'row': '2', 'col': '6', 'col_other': '2', 'criterion': 'equal', 'value': '2000', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'year', '2000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record is equal to 2000 .', 'tostr': 'filter_eq { all_rows ; year ; 2000 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; year ; 2000 } }', 'tointer': 'select the rows whose year record is equal to 2000 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'year', '2000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record is equal to 2000 .', 'tostr': 'filter_eq { all_rows ; year ; 2000 }'}, 'name'], 'result': 'anthony thomas', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; year ; 2000 } ; name }'}, 'anthony thomas'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; year ; 2000 } ; name } ; anthony thomas }', 'tointer': 'the name record of this unqiue row is anthony thomas .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; year ; 2000 } } ; eq { hop { filter_eq { all_rows ; year ; 2000 } ; name } ; anthony thomas } } = true', 'tointer': 'select the rows whose year record is equal to 2000 . there is only one such row in the table . the name record of this unqiue row is anthony thomas .'} | and { only { filter_eq { all_rows ; year ; 2000 } } ; eq { hop { filter_eq { all_rows ; year ; 2000 } ; name } ; anthony thomas } } = true | select the rows whose year record is equal to 2000 . there is only one such row in the table . the name record of this unqiue row is anthony thomas . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'year_7': 7, '2000_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'anthony thomas_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'year_7': 'year', '2000_8': '2000', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'anthony thomas_10': 'anthony thomas'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'year_7': [0], '2000_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'anthony thomas_10': [3]} | ['rank', 'name', 'attempts', 'net yds', 'yds / att', 'year'] | [['1', 'tim biakabutuka', '303', '1818', '6.0', '1995'], ['2', 'anthony thomas', '319', '1733', '5.4', '2000'], ['3', 'jamie morris', '282', '1703', '6.0', '1987'], ['4', 'denard robinson', '256', '1702', '6.6', '2010'], ['5', 'chris perry', '338', '1674', '5.0', '2003']] |
15th united states congress | https://en.wikipedia.org/wiki/15th_United_States_Congress | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-225098-4.html.csv | count | in the 15th united states congress , when the reason for change was resignation , there were 4 successors that were seated in november . | {'scope': 'subset', 'criterion': 'equal', 'value': 'november', 'result': '4', 'col': '5', 'subset': {'col': '3', 'criterion': 'fuzzily_match', 'value': 'resigned'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'reason for change', 'resigned'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; reason for change ; resigned }', 'tointer': 'select the rows whose reason for change record fuzzily matches to resigned .'}, 'date successor seated', 'november'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose reason for change record fuzzily matches to resigned . among these rows , select the rows whose date successor seated record fuzzily matches to november .', 'tostr': 'filter_eq { filter_eq { all_rows ; reason for change ; resigned } ; date successor seated ; november }'}], 'result': '4', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; reason for change ; resigned } ; date successor seated ; november } }', 'tointer': 'select the rows whose reason for change record fuzzily matches to resigned . among these rows , select the rows whose date successor seated record fuzzily matches to november . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; reason for change ; resigned } ; date successor seated ; november } } ; 4 } = true', 'tointer': 'select the rows whose reason for change record fuzzily matches to resigned . among these rows , select the rows whose date successor seated record fuzzily matches to november . the number of such rows is 4 .'} | eq { count { filter_eq { filter_eq { all_rows ; reason for change ; resigned } ; date successor seated ; november } } ; 4 } = true | select the rows whose reason for change record fuzzily matches to resigned . among these rows , select the rows whose date successor seated record fuzzily matches to november . the number of such rows is 4 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'reason for change_6': 6, 'resigned_7': 7, 'date successor seated_8': 8, 'november_9': 9, '4_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'reason for change_6': 'reason for change', 'resigned_7': 'resigned', 'date successor seated_8': 'date successor seated', 'november_9': 'november', '4_10': '4'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'reason for change_6': [0], 'resigned_7': [0], 'date successor seated_8': [1], 'november_9': [1], '4_10': [3]} | ['district', 'vacator', 'reason for change', 'successor', 'date successor seated'] | [['connecticut at - large', 'uriel holmes ( f )', 'resigned sometime in 1818', 'sylvester gilbert ( dr )', 'seated november 16 , 1818'], ['north carolina 11th', 'daniel forney ( dr )', 'resigned sometime in 1818', 'william davidson ( f )', 'seated december 2 , 1818'], ['massachusetts 20th', 'albion k parris ( dr', 'resigned february 3 , 1818', 'enoch lincoln ( dr )', 'seated november 4 , 1818'], ['virginia 19th', 'peterson goodwyn ( dr )', 'died february 21 , 1818', 'john pegram ( dr )', 'seated april 21 , 1818'], ['louisiana at - large', 'thomas b robertson ( dr )', 'resigned april 20 , 1818', 'thomas butler ( dr )', 'seated november 16 , 1818'], ['pennsylvania 4th', 'jacob spangler ( dr )', 'resigned april 20 , 1818', 'jacob hostetter ( dr )', 'seated november 16 , 1818'], ['pennsylvania 6th', 'samuel d ingham ( dr )', 'resigned july 6 , 1818', 'samuel moore ( dr )', 'seated october 13 , 1818']] |
the sunday night project | https://en.wikipedia.org/wiki/The_Sunday_Night_Project | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1590967-7.html.csv | ordinal | the 3rd programme to be shown had nerd as its musical guest . | {'row': '3', 'col': '1', 'order': '3', 'col_other': '4', 'max_or_min': 'min_to_max', 'value_mentioned': 'yes', 'scope': 'all', 'subset': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'nth_min', 'args': ['all_rows', 'episode number', '3'], 'result': '3', 'ind': 0, 'tostr': 'nth_min { all_rows ; episode number ; 3 }', 'tointer': 'the 3rd minimum episode number record of all rows is 3 .'}, '3'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_min { all_rows ; episode number ; 3 } ; 3 }', 'tointer': 'the 3rd minimum episode number record of all rows is 3 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'episode number', '3'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; episode number ; 3 }'}, 'musical guest ( song performed )'], 'result': 'nerd ( everyone nose )', 'ind': 3, 'tostr': 'hop { nth_argmin { all_rows ; episode number ; 3 } ; musical guest ( song performed ) }'}, 'nerd ( everyone nose )'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { nth_argmin { all_rows ; episode number ; 3 } ; musical guest ( song performed ) } ; nerd ( everyone nose ) }', 'tointer': 'the musical guest ( song performed ) record of the row with 3rd minimum episode number record is nerd ( everyone nose ) .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { nth_min { all_rows ; episode number ; 3 } ; 3 } ; eq { hop { nth_argmin { all_rows ; episode number ; 3 } ; musical guest ( song performed ) } ; nerd ( everyone nose ) } } = true', 'tointer': 'the 3rd minimum episode number record of all rows is 3 . the musical guest ( song performed ) record of the row with 3rd minimum episode number record is nerd ( everyone nose ) .'} | and { eq { nth_min { all_rows ; episode number ; 3 } ; 3 } ; eq { hop { nth_argmin { all_rows ; episode number ; 3 } ; musical guest ( song performed ) } ; nerd ( everyone nose ) } } = true | the 3rd minimum episode number record of all rows is 3 . the musical guest ( song performed ) record of the row with 3rd minimum episode number record is nerd ( everyone nose ) . | 6 | 6 | {'and_5': 5, 'result_6': 6, 'eq_1': 1, 'nth_min_0': 0, 'all_rows_7': 7, 'episode number_8': 8, '3_9': 9, '3_10': 10, 'str_eq_4': 4, 'str_hop_3': 3, 'nth_argmin_2': 2, 'all_rows_11': 11, 'episode number_12': 12, '3_13': 13, 'musical guest (song performed)_14': 14, 'nerd ( everyone nose )_15': 15} | {'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'nth_min_0': 'nth_min', 'all_rows_7': 'all_rows', 'episode number_8': 'episode number', '3_9': '3', '3_10': '3', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'nth_argmin_2': 'nth_argmin', 'all_rows_11': 'all_rows', 'episode number_12': 'episode number', '3_13': '3', 'musical guest (song performed)_14': 'musical guest ( song performed )', 'nerd ( everyone nose )_15': 'nerd ( everyone nose )'} | {'and_5': [6], 'result_6': [], 'eq_1': [5], 'nth_min_0': [1], 'all_rows_7': [0], 'episode number_8': [0], '3_9': [0], '3_10': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'nth_argmin_2': [3], 'all_rows_11': [2], 'episode number_12': [2], '3_13': [2], 'musical guest (song performed)_14': [3], 'nerd ( everyone nose )_15': [4]} | ['episode number', 'air date', 'guest host', 'musical guest ( song performed )', 'coat of cash wearing celebrity'] | [['1', '8 june 2008', 'katie price and peter andre', "the courteeners ( no you did n't , no you do n't )", 'andy abraham'], ['2', '15 june 2008', 'pamela anderson', "five o'clock heroes feat agyness deyn ( who )", 'ricky whittle'], ['3', '22 june 2008', 'mark ronson', 'nerd ( everyone nose )', 'stephanie mcmichael'], ['4', '29 june 2008', 'ronan keating , stephen gately , and shane lynch', 'estelle ( no substitute love )', 'carly stratton'], ['5', '6 july 2008', 'david hasselhoff', 'the feeling ( turn it up )', 'sylvia barrie'], ['6', '13 july 2008', 'barbara windsor', 'the ting tings ( shut up and let me go )', 'jennifer clark'], ['7', '20 july 2008', 'will young', "scouting for girls ( it 's not about you )", 'mario marconi']] |
solids with icosahedral symmetry | https://en.wikipedia.org/wiki/Solids_with_icosahedral_symmetry | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13727381-6.html.csv | comparative | a truncated icosahedron has less edges than a truncated icosidodecahedron . | {'row_1': '3', 'row_2': '5', 'col': '4', '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', 'dual archimedean solid', 'truncated icosahedron'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose dual archimedean solid record fuzzily matches to truncated icosahedron .', 'tostr': 'filter_eq { all_rows ; dual archimedean solid ; truncated icosahedron }'}, 'edges'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; dual archimedean solid ; truncated icosahedron } ; edges }', 'tointer': 'select the rows whose dual archimedean solid record fuzzily matches to truncated icosahedron . take the edges record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'dual archimedean solid', 'truncated icosidodecahedron'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose dual archimedean solid record fuzzily matches to truncated icosidodecahedron .', 'tostr': 'filter_eq { all_rows ; dual archimedean solid ; truncated icosidodecahedron }'}, 'edges'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; dual archimedean solid ; truncated icosidodecahedron } ; edges }', 'tointer': 'select the rows whose dual archimedean solid record fuzzily matches to truncated icosidodecahedron . take the edges record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; dual archimedean solid ; truncated icosahedron } ; edges } ; hop { filter_eq { all_rows ; dual archimedean solid ; truncated icosidodecahedron } ; edges } } = true', 'tointer': 'select the rows whose dual archimedean solid record fuzzily matches to truncated icosahedron . take the edges record of this row . select the rows whose dual archimedean solid record fuzzily matches to truncated icosidodecahedron . take the edges record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; dual archimedean solid ; truncated icosahedron } ; edges } ; hop { filter_eq { all_rows ; dual archimedean solid ; truncated icosidodecahedron } ; edges } } = true | select the rows whose dual archimedean solid record fuzzily matches to truncated icosahedron . take the edges record of this row . select the rows whose dual archimedean solid record fuzzily matches to truncated icosidodecahedron . take the edges 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, 'dual archimedean solid_7': 7, 'truncated icosahedron_8': 8, 'edges_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'dual archimedean solid_11': 11, 'truncated icosidodecahedron_12': 12, 'edges_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', 'dual archimedean solid_7': 'dual archimedean solid', 'truncated icosahedron_8': 'truncated icosahedron', 'edges_9': 'edges', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'dual archimedean solid_11': 'dual archimedean solid', 'truncated icosidodecahedron_12': 'truncated icosidodecahedron', 'edges_13': 'edges'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'dual archimedean solid_7': [0], 'truncated icosahedron_8': [0], 'edges_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'dual archimedean solid_11': [1], 'truncated icosidodecahedron_12': [1], 'edges_13': [3]} | ['picture', 'dual archimedean solid', 'faces', 'edges', 'vertices', 'face polygon'] | [['( video )', 'icosidodecahedron', '30', '60', '32', 'rhombus'], ['( video )', 'truncated dodecahedron', '60', '90', '32', 'isosceles triangle'], ['( video )', 'truncated icosahedron', '60', '90', '32', 'isosceles triangle'], ['( video )', 'rhombicosidodecahedron', '60', '120', '62', 'kite'], ['( video )', 'truncated icosidodecahedron', '120', '180', '62', 'scalene triangle']] |
media in victoria , british columbia | https://en.wikipedia.org/wiki/Media_in_Victoria%2C_British_Columbia | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18409499-1.html.csv | comparative | radio victoria uses a higher frequency than the frequency that kool fm runs on . | {'row_1': '12', 'row_2': '11', 'col': '1', 'col_other': '3', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'branding', 'radio victoria'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose branding record fuzzily matches to radio victoria .', 'tostr': 'filter_eq { all_rows ; branding ; radio victoria }'}, 'frequency'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; branding ; radio victoria } ; frequency }', 'tointer': 'select the rows whose branding record fuzzily matches to radio victoria . take the frequency record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'branding', 'kool fm'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose branding record fuzzily matches to kool fm .', 'tostr': 'filter_eq { all_rows ; branding ; kool fm }'}, 'frequency'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; branding ; kool fm } ; frequency }', 'tointer': 'select the rows whose branding record fuzzily matches to kool fm . take the frequency record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; branding ; radio victoria } ; frequency } ; hop { filter_eq { all_rows ; branding ; kool fm } ; frequency } } = true', 'tointer': 'select the rows whose branding record fuzzily matches to radio victoria . take the frequency record of this row . select the rows whose branding record fuzzily matches to kool fm . take the frequency record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; branding ; radio victoria } ; frequency } ; hop { filter_eq { all_rows ; branding ; kool fm } ; frequency } } = true | select the rows whose branding record fuzzily matches to radio victoria . take the frequency record of this row . select the rows whose branding record fuzzily matches to kool fm . take the frequency 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, 'branding_7': 7, 'radio victoria_8': 8, 'frequency_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'branding_11': 11, 'kool fm_12': 12, 'frequency_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', 'branding_7': 'branding', 'radio victoria_8': 'radio victoria', 'frequency_9': 'frequency', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'branding_11': 'branding', 'kool fm_12': 'kool fm', 'frequency_13': 'frequency'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'branding_7': [0], 'radio victoria_8': [0], 'frequency_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'branding_11': [1], 'kool fm_12': [1], 'frequency_13': [3]} | ['frequency', 'call sign', 'branding', 'format', 'owner'] | [['am 1070', 'cfax', 'cfax 1070', 'news / talk', 'bell media radio'], ['fm 88.9', 'cbux - fm - 1', 'espace musique', 'public music', 'canadian broadcasting corporation'], ['fm 90.5', 'cbcv - fm', 'cbc radio one', 'public news / talk', 'canadian broadcasting corporation'], ['fm 91.3', 'cjzn - fm', 'the zone', 'modern rock', 'jim pattison group'], ['fm 92.1', 'cbu - fm - 2', 'cbc radio 2', 'public music', 'canadian broadcasting corporation'], ['fm 98.5', 'cioc - fm', 'the ocean', 'soft adult contemporary', 'rogers communications'], ['fm 99.7', 'cbuf - fm - 9', 'première chaîne', 'public news / talk', 'canadian broadcasting corporation'], ['fm 100.3', 'ckkq - fm', 'the q', 'active rock', 'jim pattison group'], ['fm 101.9', 'cfuv - fm', 'cfuv', 'campus radio', 'university of victoria'], ['fm 103.1', 'chtt - fm', 'jack fm', 'adult hits', 'rogers communications'], ['fm 107.3', 'chbe - fm', 'kool fm', 'hot adult contemporary', 'bell media radio'], ['fm 107.9', 'cils - fm', 'radio victoria', 'community radio', 'société radio communautaire victoria']] |
1930 - 31 chicago black hawks season | https://en.wikipedia.org/wiki/1930%E2%80%9331_Chicago_Black_Hawks_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12791739-5.html.csv | count | the chicago black hawks were the visitors for three games . | {'scope': 'all', 'criterion': 'equal', 'value': 'chicago black hawks', 'result': '3', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'visitor', 'chicago black hawks'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose visitor record fuzzily matches to chicago black hawks .', 'tostr': 'filter_eq { all_rows ; visitor ; chicago black hawks }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; visitor ; chicago black hawks } }', 'tointer': 'select the rows whose visitor record fuzzily matches to chicago black hawks . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; visitor ; chicago black hawks } } ; 3 } = true', 'tointer': 'select the rows whose visitor record fuzzily matches to chicago black hawks . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; visitor ; chicago black hawks } } ; 3 } = true | select the rows whose visitor record fuzzily matches to chicago black hawks . 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, 'visitor_5': 5, 'chicago black hawks_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', 'visitor_5': 'visitor', 'chicago black hawks_6': 'chicago black hawks', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'visitor_5': [0], 'chicago black hawks_6': [0], '3_7': [2]} | ['date', 'visitor', 'score', 'home', 'record'] | [['april 3', 'montreal canadiens', '2 - 1', 'chicago black hawks', '0 - 1'], ['april 5', 'montreal canadiens', '1 - 2', 'chicago black hawks', '1 - 1'], ['april 9', 'chicago black hawks', '3 - 2', 'montreal canadiens', '2 - 1'], ['april 11', 'chicago black hawks', '2 - 4', 'montreal canadiens', '2 - 2'], ['april 13', 'chicago black hawks', '0 - 2', 'montreal canadiens', '2 - 3']] |
athletics at the 2008 summer olympics - men 's 400 metres | https://en.wikipedia.org/wiki/Athletics_at_the_2008_Summer_Olympics_%E2%80%93_Men%27s_400_metres | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18569105-8.html.csv | unique | the only person that had a reaction time over 48 seconds was naiel santiago d'almeida . | {'scope': 'all', 'row': '8', 'col': '5', 'col_other': '3', 'criterion': 'greater_than', 'value': '48', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'time', '48'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose time record is greater than 48 .', 'tostr': 'filter_greater { all_rows ; time ; 48 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; time ; 48 } }', 'tointer': 'select the rows whose time record is greater than 48 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'time', '48'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose time record is greater than 48 .', 'tostr': 'filter_greater { all_rows ; time ; 48 }'}, 'athlete'], 'result': "naiel santiago d'almeida", 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; time ; 48 } ; athlete }'}, "naiel santiago d'almeida"], 'result': True, 'ind': 3, 'tostr': "eq { hop { filter_greater { all_rows ; time ; 48 } ; athlete } ; naiel santiago d'almeida }", 'tointer': "the athlete record of this unqiue row is naiel santiago d'almeida ."}], 'result': True, 'ind': 4, 'tostr': "and { only { filter_greater { all_rows ; time ; 48 } } ; eq { hop { filter_greater { all_rows ; time ; 48 } ; athlete } ; naiel santiago d'almeida } } = true", 'tointer': "select the rows whose time record is greater than 48 . there is only one such row in the table . the athlete record of this unqiue row is naiel santiago d'almeida ."} | and { only { filter_greater { all_rows ; time ; 48 } } ; eq { hop { filter_greater { all_rows ; time ; 48 } ; athlete } ; naiel santiago d'almeida } } = true | select the rows whose time record is greater than 48 . there is only one such row in the table . the athlete record of this unqiue row is naiel santiago d'almeida . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'time_7': 7, '48_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'athlete_9': 9, "naiel santiago d'almeida_10": 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'time_7': 'time', '48_8': '48', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'athlete_9': 'athlete', "naiel santiago d'almeida_10": "naiel santiago d'almeida"} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'time_7': [0], '48_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'athlete_9': [2], "naiel santiago d'almeida_10": [3]} | ['rank', 'lane', 'athlete', 'nationality', 'time', 'react'] | [['1', '9', 'jeremy wariner', 'united states', '45.23', '0.253'], ['2', '6', 'tabarie henry', 'virgin islands', '45.36', '0.165'], ['3', '2', 'cedric van branteghem', 'belgium', '45.54', '0.203'], ['4', '4', 'david gillick', 'ireland', '45.83', '0.275'], ['5', '5', 'maksim dyldin', 'russia', '46.03', '0.194'], ['6', '3', 'myhaylo knysh', 'ukraine', '46.28', '0.260'], ['7', '7', 'mathieu gnanligo', 'benin', '47.10', '0.207'], ['8', '8', "naiel santiago d'almeida", 'são tomé and príncipe', '49.08', '0.178']] |
mobile telephony in africa | https://en.wikipedia.org/wiki/Mobile_telephony_in_Africa | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-29395291-2.html.csv | count | there are two different network providers for the country of nigeria . | {'scope': 'subset', 'criterion': 'equal', 'value': 'nigeria', 'result': '2', 'col': '2', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'nigeria'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'nigeria'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; country ; nigeria }', 'tointer': 'select the rows whose country record fuzzily matches to nigeria .'}, 'country', 'nigeria'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose country record fuzzily matches to nigeria . among these rows , select the rows whose country record fuzzily matches to nigeria .', 'tostr': 'filter_eq { filter_eq { all_rows ; country ; nigeria } ; country ; nigeria }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; country ; nigeria } ; country ; nigeria } }', 'tointer': 'select the rows whose country record fuzzily matches to nigeria . among these rows , select the rows whose country record fuzzily matches to nigeria . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; country ; nigeria } ; country ; nigeria } } ; 2 } = true', 'tointer': 'select the rows whose country record fuzzily matches to nigeria . among these rows , select the rows whose country record fuzzily matches to nigeria . the number of such rows is 2 .'} | eq { count { filter_eq { filter_eq { all_rows ; country ; nigeria } ; country ; nigeria } } ; 2 } = true | select the rows whose country record fuzzily matches to nigeria . among these rows , select the rows whose country record fuzzily matches to nigeria . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'country_6': 6, 'nigeria_7': 7, 'country_8': 8, 'nigeria_9': 9, '2_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'country_6': 'country', 'nigeria_7': 'nigeria', 'country_8': 'country', 'nigeria_9': 'nigeria', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'country_6': [0], 'nigeria_7': [0], 'country_8': [1], 'nigeria_9': [1], '2_10': [3]} | ['provider', 'country', 'subscribers ( 2005 ) ( thousands )', 'subscribers ( 2006 ) ( thousands )', 'growth %'] | [['airtel', 'kenya , uganda', '37600', '31800', '54.9'], ['vodacom', 'south africa', '17600', '21800', '23.9'], ['mtn', 'south africa', '10235', '12483', '22'], ['mtn', 'nigeria', '8370', '12281', '47'], ['glo mobile', 'nigeria', '9000', '11000', '22'], ['maroc', 'morocco', '8237', '10707', '30'], ['djezzy', 'algeria', '7109', '10531', '48'], ['mobinil', 'egypt', '66960', '9267', '38'], ['vodafone', 'egypt', '6125', '8704', '42'], ['mobilis', 'algeria', '4908', '7476', '52']] |
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 | count | there are eight public institution members of the lone star alliance . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'public', 'result': '8', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'affiliation', 'public'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose affiliation record fuzzily matches to public .', 'tostr': 'filter_eq { all_rows ; affiliation ; public }'}], 'result': '8', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; affiliation ; public } }', 'tointer': 'select the rows whose affiliation record fuzzily matches to public . the number of such rows is 8 .'}, '8'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; affiliation ; public } } ; 8 } = true', 'tointer': 'select the rows whose affiliation record fuzzily matches to public . the number of such rows is 8 .'} | eq { count { filter_eq { all_rows ; affiliation ; public } } ; 8 } = true | select the rows whose affiliation record fuzzily matches to public . the number of such rows is 8 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'affiliation_5': 5, 'public_6': 6, '8_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'affiliation_5': 'affiliation', 'public_6': 'public', '8_7': '8'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'affiliation_5': [0], 'public_6': [0], '8_7': [2]} | ['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 )']] |
1947 vfl season | https://en.wikipedia.org/wiki/1947_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10809444-4.html.csv | majority | the majority of the time the crowd was over 10,000 people . | {'scope': 'all', 'col': '6', 'most_or_all': 'all', 'criterion': 'greater_than', 'value': '10000', 'subset': None} | {'func': 'all_greater', 'args': ['all_rows', 'crowd', '10000'], 'result': True, 'ind': 0, 'tointer': 'for the crowd records of all rows , all of them are greater than 10000 .', 'tostr': 'all_greater { all_rows ; crowd ; 10000 } = true'} | all_greater { all_rows ; crowd ; 10000 } = true | for the crowd records of all rows , all of them are greater than 10000 . | 1 | 1 | {'all_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'crowd_3': 3, '10000_4': 4} | {'all_greater_0': 'all_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'crowd_3': 'crowd', '10000_4': '10000'} | {'all_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'crowd_3': [0], '10000_4': [0]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['collingwood', '19.24 ( 138 )', 'south melbourne', '12.14 ( 86 )', 'victoria park', '25000', '10 may 1947'], ['carlton', '16.16 ( 112 )', 'footscray', '7.16 ( 58 )', 'princes park', '37000', '10 may 1947'], ['richmond', '10.12 ( 72 )', 'melbourne', '18.12 ( 120 )', 'punt road oval', '16000', '10 may 1947'], ['st kilda', '10.17 ( 77 )', 'hawthorn', '12.12 ( 84 )', 'junction oval', '10500', '10 may 1947'], ['north melbourne', '4.15 ( 39 )', 'fitzroy', '19.26 ( 140 )', 'arden street oval', '12000', '10 may 1947'], ['geelong', '11.17 ( 83 )', 'essendon', '16.19 ( 115 )', 'kardinia park', '14500', '10 may 1947']] |
lark rise to candleford ( tv series ) | https://en.wikipedia.org/wiki/Lark_Rise_to_Candleford_%28TV_series%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15026994-2.html.csv | aggregation | the average viewing figures for lark rise to candleford over it 's nine episodes was 6.73 million . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '6.73 million', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'viewing figure'], 'result': '6.73 million', 'ind': 0, 'tostr': 'avg { all_rows ; viewing figure }'}, '6.73 million'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; viewing figure } ; 6.73 million } = true', 'tointer': 'the average of the viewing figure record of all rows is 6.73 million .'} | round_eq { avg { all_rows ; viewing figure } ; 6.73 million } = true | the average of the viewing figure record of all rows is 6.73 million . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'viewing figure_4': 4, '6.73 million_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'viewing figure_4': 'viewing figure', '6.73 million_5': '6.73 million'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'viewing figure_4': [0], '6.73 million_5': [1]} | ['', 'episode', 'writer', 'director', 'original air date', 'viewing figure'] | [['1', 'episode 1', 'bill gallagher', 'charles palmer', '13 january 2008', '7.27 million'], ['2', 'episode 2', 'bill gallagher', 'charles palmer', '20 january 2008', '7.01 million'], ['3', 'episode 3', 'bill gallagher', 'charles palmer', '27 january 2008', '6.66 million'], ['4', 'episode 4', 'paul rutman', 'john greening', '3 february 2008', '6.72 million'], ['5', 'episode 5', 'bill gallagher', 'charles palmer', '10 february 2008', '6.85 million'], ['6', 'episode 6', 'bill gallagher', 'john greening', '17 february 2008', '6.68 million'], ['7', 'episode 7', 'carolyn bonnyman', 'marc jobst', '24 february 2008', '6.70 million'], ['8', 'episode 8', 'gaby chiappe', 'marc jobst', '2 march 2008', '6.48 million'], ['9', 'episode 9', 'bill gallagher', 'john greening', '9 march 2008', '6.21 million']] |
28th united states congress | https://en.wikipedia.org/wiki/28th_United_States_Congress | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-225206-3.html.csv | count | a total of two seats that were vacated during the 28th united states congress were not filled during the term . | {'scope': 'all', 'criterion': 'equal', 'value': 'not filled this term', 'result': '2', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date of successors formal installation', 'not filled this term'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date of successors formal installation record fuzzily matches to not filled this term .', 'tostr': 'filter_eq { all_rows ; date of successors formal installation ; not filled this term }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; date of successors formal installation ; not filled this term } }', 'tointer': 'select the rows whose date of successors formal installation record fuzzily matches to not filled this term . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; date of successors formal installation ; not filled this term } } ; 2 } = true', 'tointer': 'select the rows whose date of successors formal installation record fuzzily matches to not filled this term . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; date of successors formal installation ; not filled this term } } ; 2 } = true | select the rows whose date of successors formal installation record fuzzily matches to not filled this term . 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, 'date of successors formal installation_5': 5, 'not filled this term_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', 'date of successors formal installation_5': 'date of successors formal installation', 'not filled this term_6': 'not filled this term', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'date of successors formal installation_5': [0], 'not filled this term_6': [0], '2_7': [2]} | ['state ( class )', 'vacator', 'reason for change', 'successor', 'date of successors formal installation'] | [['tennessee ( 2 )', 'vacant', 'failure to elect', 'spencer jarnagin ( w )', 'elected october 17 , 1843'], ['maine ( 1 )', 'vacant', 'rep reuel williams resigned in previous congress', 'john fairfield ( d )', 'elected december 4 , 1843'], ['illinois ( 2 )', 'samuel mcroberts ( d )', 'died march 27 , 1843', 'james semple ( d )', 'elected december 4 , 1843'], ['missouri ( 3 )', 'lewis f linn ( d )', 'died october 3 , 1843', 'david r atchison ( d )', 'elected december 14 , 1843'], ['rhode island ( 1 )', 'william sprague ( d )', 'resigned january 17 , 1844', 'john b francis ( lo )', 'elected january 25 , 1844'], ['arkansas ( 2 )', 'william s fulton ( d )', 'died august 15 , 1844', 'chester ashley ( d )', 'elected november 8 , 1844'], ['new york ( 3 )', 'henry a foster ( d )', 'successor elected january 27 , 1845', 'john a dix ( d )', 'elected january 27 , 1845'], ['south carolina ( 2 )', 'daniel e huger ( d )', 'resigned march 3 , 1845', 'vacant', 'not filled this term'], ['florida ( 1 )', 'vacant', 'florida admitted to the union march 3 , 1845', 'vacant', 'not filled this term']] |
1947 in brazilian football | https://en.wikipedia.org/wiki/1947_in_Brazilian_football | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15318779-1.html.csv | aggregation | the average points earned by brazilian football teams in 1947 was about 21.2 . | {'scope': 'all', 'col': '3', 'type': 'average', 'result': '21.2', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'points'], 'result': '21.2', 'ind': 0, 'tostr': 'avg { all_rows ; points }'}, '21.2'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; points } ; 21.2 } = true', 'tointer': 'the average of the points record of all rows is 21.2 .'} | round_eq { avg { all_rows ; points } ; 21.2 } = true | the average of the points record of all rows is 21.2 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'points_4': 4, '21.2_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'points_4': 'points', '21.2_5': '21.2'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'points_4': [0], '21.2_5': [1]} | ['position', 'team', 'points', 'played', 'won', 'drawn', 'lost', 'for', 'against', 'difference'] | [['1', 'palmeiras', '36', '20', '17', '2', '1', '51', '16', '35'], ['2', 'corinthians', '32', '20', '14', '4', '2', '54', '19', '35'], ['3', 'portuguesa', '27', '20', '11', '5', '4', '43', '28', '15'], ['4', 'são paulo', '25', '20', '8', '9', '3', '48', '27', '21'], ['5', 'ypiranga - sp', '21', '20', '9', '3', '8', '36', '26', '10'], ['6', 'santos', '19', '20', '6', '7', '7', '33', '27', '6'], ['7', 'juventus', '16', '20', '5', '6', '9', '29', '45', '- 16'], ['8', 'portuguesa santista', '15', '20', '6', '3', '11', '27', '42', '- 15'], ['9', 'comercial - sp', '11', '20', '5', '1', '14', '25', '59', '- 34'], ['10', 'nacional - sp', '10', '20', '3', '4', '13', '25', '47', '- 22']] |
asier peña iturria | https://en.wikipedia.org/wiki/Asier_Pe%C3%B1a_Iturria | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15289953-1.html.csv | count | 2 of the men 's speed skating events took place in calgary . | {'scope': 'all', 'criterion': 'equal', 'value': 'calgary', 'result': '2', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'calgary'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record fuzzily matches to calgary .', 'tostr': 'filter_eq { all_rows ; location ; calgary }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; location ; calgary } }', 'tointer': 'select the rows whose location record fuzzily matches to calgary . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; location ; calgary } } ; 2 } = true', 'tointer': 'select the rows whose location record fuzzily matches to calgary . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; location ; calgary } } ; 2 } = true | select the rows whose location record fuzzily matches to calgary . 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, 'location_5': 5, 'calgary_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', 'location_5': 'location', 'calgary_6': 'calgary', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'location_5': [0], 'calgary_6': [0], '2_7': [2]} | ['distance', 'time', 'date', 'location', 'notes'] | [["men 's speed skating", "men 's speed skating", "men 's speed skating", "men 's speed skating", "men 's speed skating"], ['distance', 'time', 'date', 'location', 'notes'], ['500 m', '40.33', '2009 - 10 - 24', 'calgary', 'spanish national record'], ['1000 m', '1:18.52', '2009 - 09 - 19', 'calgary', 'spanish national record'], ['1500 m', '1:58.24', '2008 - 01 - 13', 'kolomna', 'spanish national record'], ['3000 m', '4:09.10', '2008 - 02 - 16', 'hamar', 'spanish national record'], ['5000 m', '6:51.72', '2009 - 12 - 12', 'salt lake city', 'spanish national record'], ['10000 m', '14:35.06', '2008 - 01 - 27', 'hamar', 'spanish national record']] |
2009 - 10 pittsburgh panthers men 's basketball team | https://en.wikipedia.org/wiki/2009%E2%80%9310_Pittsburgh_Panthers_men%27s_basketball_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-24925945-3.html.csv | superlative | for the 2009 - 10 pittsburgh panthers men 's basketball team , the player with the highest weight is gary mcghee . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '6', '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', 'weight ( lb )'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; weight ( lb ) }'}, 'name'], 'result': 'gary mcghee', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; weight ( lb ) } ; name }'}, 'gary mcghee'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; weight ( lb ) } ; name } ; gary mcghee } = true', 'tointer': 'select the row whose weight ( lb ) record of all rows is maximum . the name record of this row is gary mcghee .'} | eq { hop { argmax { all_rows ; weight ( lb ) } ; name } ; gary mcghee } = true | select the row whose weight ( lb ) record of all rows is maximum . the name record of this row is gary mcghee . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'weight ( lb )_5': 5, 'name_6': 6, 'gary mcghee_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'weight ( lb )_5': 'weight ( lb )', 'name_6': 'name', 'gary mcghee_7': 'gary mcghee'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'weight ( lb )_5': [0], 'name_6': [1], 'gary mcghee_7': [2]} | ['name', '-', 'position', 'height', 'weight ( lb )', 'year', 'hometown', 'previous school'] | [['chase adams', '3', 'guard', 'ft10in ( m )', '190', '2 senior', 'baltimore , md', 'centenary / mount saint joseph hs'], ['gilbert brown', '5', 'guard / forward', 'ft6in ( m )', '200', '2 junior ( rs )', 'harrisburg , pa', 'south kent school'], ['jermaine dixon', '24', 'guard', 'ft3in ( m )', '200', '3 senior , transfer', 'baltimore , md', 'tallahassee cc / maine central inst / blake hs'], ['tim frye', '44', 'guard', 'ft4in ( m )', '205', '2 junior', 'mars , pa', 'mars area hs'], ['ashton gibbs', '12', 'guard', 'ft2in ( m )', '190', '1 sophomore', 'scotch plains , nj', 'seton hall prep'], ['gary mcghee', '52', 'center', 'ft10in ( m )', '250', '2 junior', 'anderson , in', 'highland hs'], ['dwight miller', '25', 'forward', 'ft8in ( m )', '240', '1 freshman ( rs )', 'nassau , bahamas', 'st pius x hs'], ['lamar patterson', '21', 'guard / forward', 'ft5in ( m )', '220', '1 freshman', 'lancaster , pa', "st benedict 's prep / jp mccaskey hs"], ['jj richardson', '55', 'forward', 'ft7in ( m )', '235', '1 freshman', 'missouri city , tx', 'fort bend hightower hs'], ['nick rivers', '14', 'guard', 'ft0in ( m )', '180', '1 junior', 'phoenix , az', 'brophy college prep'], ['nasir robinson', '35', 'forward', 'ft5in ( m )', '220', '1 sophomore', 'chester , pa', 'chester hs'], ['dante taylor', '11', 'forward', 'ft9in ( m )', '240', '1 freshman', 'greenburgh , ny', 'national christian academy ( md )'], ['brad wanamaker', '22', 'guard', 'ft4in ( m )', '210', '2 junior', 'philadelphia , pa', 'roman catholic hs'], ['travon woodall', '1', 'guard', 'ft11in ( m )', '190', '1 freshman ( rs )', 'brooklyn , ny', 'st anthony hs']] |
2008 - 09 los angeles clippers season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Los_Angeles_Clippers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17323529-5.html.csv | majority | all games of the 2008 - 09 los angeles clippers ' season were scheduled for the month of november . | {'scope': 'all', 'col': '2', 'most_or_all': 'all', 'criterion': 'fuzzily_match', 'value': 'november', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'date', 'november'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , all of them fuzzily match to november .', 'tostr': 'all_eq { all_rows ; date ; november } = true'} | all_eq { all_rows ; date ; november } = true | for the date records of all rows , all of them fuzzily match to november . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, 'november_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', 'november_4': 'november'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], 'november_4': [0]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['3', 'november 1', 'utah', 'l 79 - 101 ( ot )', 'cuttino mobley ( 20 )', 'chris kaman ( 12 )', 'mike taylor ( 4 )', 'energysolutions arena 19602', '0 - 3'], ['4', 'november 3', 'utah', 'l 73 - 89 ( ot )', 'chris kaman ( 19 )', 'chris kaman ( 10 )', 'baron davis ( 9 )', 'staples center 12712', '0 - 4'], ['5', 'november 5', 'la lakers', 'l 88 - 106 ( ot )', 'al thornton ( 22 )', 'tim thomas , chris kaman ( 11 )', 'baron davis ( 7 )', 'staples center 18997', '0 - 5'], ['6', 'november 7', 'houston', 'l 83 - 92 ( ot )', 'baron davis , chris kaman ( 23 )', 'marcus camby ( 13 )', 'baron davis ( 8 )', 'staples center 14670', '0 - 6'], ['7', 'november 9', 'dallas', 'w 103 - 92 ( ot )', 'baron davis ( 22 )', 'marcus camby ( 14 )', 'baron davis ( 10 )', 'staples center 14249', '1 - 6'], ['8', 'november 12', 'sacramento', 'l 98 - 103 ( ot )', 'al thornton ( 20 )', 'chris kaman ( 6 )', 'baron davis ( 11 )', 'staples center 13266', '1 - 7'], ['9', 'november 15', 'golden state', 'l 103 - 121 ( ot )', 'baron davis ( 25 )', 'chris kaman ( 13 )', 'baron davis ( 11 )', 'staples center 12823', '1 - 8'], ['10', 'november 17', 'san antonio', 'l 83 - 86 ( ot )', 'cuttino mobley ( 18 )', 'chris kaman ( 13 )', 'baron davis ( 8 )', 'staples center 14962', '1 - 9'], ['11', 'november 19', 'oklahoma city', 'w 108 - 88 ( ot )', 'chris kaman ( 25 )', 'chris kaman ( 14 )', 'baron davis ( 8 )', 'ford center 18312', '2 - 9'], ['12', 'november 21', 'philadelphia', 'l 88 - 89 ( ot )', 'al thornton ( 22 )', 'al thornton , chris kaman , marcus camby ( 9 )', 'baron davis ( 6 )', 'wachovia center 13474', '2 - 10'], ['13', 'november 22', 'new jersey', 'l 95 - 112 ( ot )', 'baron davis ( 30 )', 'marcus camby ( 13 )', 'baron davis ( 10 )', 'izod center 17677', '2 - 11'], ['14', 'november 24', 'new orleans', 'l 87 - 99 ( ot )', 'eric gordon ( 25 )', 'marcus camby ( 11 )', 'baron davis ( 8 )', 'staples center 14956', '2 - 12'], ['15', 'november 26', 'denver', 'l 105 - 106 ( ot )', 'eric gordon ( 24 )', 'marcus camby ( 11 )', 'baron davis ( 10 )', 'staples center 14934', '2 - 13'], ['16', 'november 29', 'miami', 'w 97 - 96 ( ot )', 'al thornton , zach randolph ( 27 )', 'zach randolph ( 13 )', 'baron davis ( 9 )', 'staples center 16245', '3 - 13']] |
mexican grand prix | https://en.wikipedia.org/wiki/Mexican_Grand_Prix | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1108831-3.html.csv | count | through the years 1962-1992 of mexican grand prix , two of the drivers used williams - renault constructor in hermanos rodriguez location . | {'scope': 'subset', 'criterion': 'equal', 'value': 'williams - renault', 'result': '2', 'col': '3', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'hermanos rodriguez'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'hermanos rodriguez'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; location ; hermanos rodriguez }', 'tointer': 'select the rows whose location record fuzzily matches to hermanos rodriguez .'}, 'constructor', 'williams - renault'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose location record fuzzily matches to hermanos rodriguez . among these rows , select the rows whose constructor record fuzzily matches to williams - renault .', 'tostr': 'filter_eq { filter_eq { all_rows ; location ; hermanos rodriguez } ; constructor ; williams - renault }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; location ; hermanos rodriguez } ; constructor ; williams - renault } }', 'tointer': 'select the rows whose location record fuzzily matches to hermanos rodriguez . among these rows , select the rows whose constructor record fuzzily matches to williams - renault . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; location ; hermanos rodriguez } ; constructor ; williams - renault } } ; 2 } = true', 'tointer': 'select the rows whose location record fuzzily matches to hermanos rodriguez . among these rows , select the rows whose constructor record fuzzily matches to williams - renault . the number of such rows is 2 .'} | eq { count { filter_eq { filter_eq { all_rows ; location ; hermanos rodriguez } ; constructor ; williams - renault } } ; 2 } = true | select the rows whose location record fuzzily matches to hermanos rodriguez . among these rows , select the rows whose constructor record fuzzily matches to williams - renault . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'location_6': 6, 'hermanos rodriguez_7': 7, 'constructor_8': 8, 'williams - renault_9': 9, '2_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'location_6': 'location', 'hermanos rodriguez_7': 'hermanos rodriguez', 'constructor_8': 'constructor', 'williams - renault_9': 'williams - renault', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'location_6': [0], 'hermanos rodriguez_7': [0], 'constructor_8': [1], 'williams - renault_9': [1], '2_10': [3]} | ['year', 'driver', 'constructor', 'location', 'report'] | [['1992', 'nigel mansell', 'williams - renault', 'hermanos rodriguez', 'report'], ['1991', 'riccardo patrese', 'williams - renault', 'hermanos rodriguez', 'report'], ['1990', 'alain prost', 'ferrari', 'hermanos rodriguez', 'report'], ['1989', 'ayrton senna', 'mclaren - honda', 'hermanos rodriguez', 'report'], ['1988', 'alain prost', 'mclaren - honda', 'hermanos rodriguez', 'report'], ['1987', 'nigel mansell', 'williams - honda', 'hermanos rodriguez', 'report'], ['1986', 'gerhard berger', 'benetton - bmw', 'hermanos rodriguez', 'report'], ['1985 - 1971', 'not held', 'not held', 'not held', 'not held'], ['1970', 'jacky ickx', 'ferrari', 'hermanos rodriguez', 'report'], ['1969', 'denny hulme', 'mclaren - ford', 'hermanos rodriguez', 'report'], ['1968', 'graham hill', 'lotus - ford', 'hermanos rodriguez', 'report'], ['1967', 'jim clark', 'lotus - ford', 'hermanos rodriguez', 'report'], ['1966', 'john surtees', 'cooper - maserati', 'hermanos rodriguez', 'report'], ['1965', 'richie ginther', 'honda', 'hermanos rodriguez', 'report'], ['1964', 'dan gurney', 'brabham - climax', 'hermanos rodriguez', 'report'], ['1963', 'jim clark', 'lotus - climax', 'hermanos rodriguez', 'report'], ['1962', 'trevor taylor jim clark', 'lotus - climax', 'magdalena mixhuca', 'report']] |
1937 vfl season | https://en.wikipedia.org/wiki/1937_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10806194-2.html.csv | aggregation | for the 1937 vfl season the total combined crowd was 100600 . | {'scope': 'all', 'col': '6', 'type': 'sum', 'result': '100600', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'crowd'], 'result': '100600', 'ind': 0, 'tostr': 'sum { all_rows ; crowd }'}, '100600'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; crowd } ; 100600 } = true', 'tointer': 'the sum of the crowd record of all rows is 100600 .'} | round_eq { sum { all_rows ; crowd } ; 100600 } = true | the sum of the crowd record of all rows is 100600 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'crowd_4': 4, '100600_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'crowd_4': 'crowd', '100600_5': '100600'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'crowd_4': [0], '100600_5': [1]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['hawthorn', '14.15 ( 99 )', 'st kilda', '15.22 ( 112 )', 'glenferrie oval', '16000', '1 may 1937'], ['fitzroy', '9.11 ( 65 )', 'north melbourne', '10.7 ( 67 )', 'brunswick street oval', '16000', '1 may 1937'], ['essendon', '13.7 ( 85 )', 'melbourne', '25.20 ( 170 )', 'windy hill', '14000', '1 may 1937'], ['richmond', '17.15 ( 117 )', 'footscray', '12.11 ( 83 )', 'punt road oval', '16000', '1 may 1937'], ['south melbourne', '11.19 ( 85 )', 'collingwood', '20.20 ( 140 )', 'lake oval', '25000', '1 may 1937'], ['geelong', '8.12 ( 60 )', 'carlton', '6.13 ( 49 )', 'corio oval', '13600', '1 may 1937']] |
2006 - 07 isu junior grand prix | https://en.wikipedia.org/wiki/2006%E2%80%9307_ISU_Junior_Grand_Prix | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12392804-3.html.csv | count | 2006 - 07 isu junior grand prix , 7 seven teams won no gold medals . | {'scope': 'all', 'criterion': 'equal', 'value': '0', 'result': '7', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'gold', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose gold record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; gold ; 0 }'}], 'result': '7', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; gold ; 0 } }', 'tointer': 'select the rows whose gold record is equal to 0 . the number of such rows is 7 .'}, '7'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; gold ; 0 } } ; 7 } = true', 'tointer': 'select the rows whose gold record is equal to 0 . the number of such rows is 7 .'} | eq { count { filter_eq { all_rows ; gold ; 0 } } ; 7 } = true | select the rows whose gold record is equal to 0 . the number of such rows is 7 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'gold_5': 5, '0_6': 6, '7_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'gold_5': 'gold', '0_6': '0', '7_7': '7'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'gold_5': [0], '0_6': [0], '7_7': [2]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'united states', '24', '12', '8', '44'], ['2', 'russia', '5', '5', '6', '16'], ['3', 'canada', '1', '2', '7', '10'], ['4', 'japan', '1', '4', '3', '8'], ['5', 'estonia', '1', '2', '1', '4'], ['5', 'italy', '0', '3', '1', '4'], ['6', 'south korea', '0', '0', '3', '3'], ['7', 'france', '0', '1', '1', '2'], ['7', 'ukraine', '0', '1', '1', '2'], ['8', 'spain', '0', '1', '0', '1'], ['8', 'china', '0', '1', '0', '1'], ['8', 'czech republic', '0', '0', '1', '1']] |
northern province , sri lanka | https://en.wikipedia.org/wiki/Northern_Province%2C_Sri_Lanka | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23777640-1.html.csv | superlative | in northern province , sri lanka , the administrative district of jaffna has the highest population density . | {'scope': 'all', 'col_superlative': '12', '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', 'population density ( / km 2 )'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; population density ( / km 2 ) }'}, 'administrative district'], 'result': 'jaffna', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; population density ( / km 2 ) } ; administrative district }'}, 'jaffna'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; population density ( / km 2 ) } ; administrative district } ; jaffna } = true', 'tointer': 'select the row whose population density ( / km 2 ) record of all rows is maximum . the administrative district record of this row is jaffna .'} | eq { hop { argmax { all_rows ; population density ( / km 2 ) } ; administrative district } ; jaffna } = true | select the row whose population density ( / km 2 ) record of all rows is maximum . the administrative district record of this row is jaffna . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'population density ( / km 2 )_5': 5, 'administrative district_6': 6, 'jaffna_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'population density ( / km 2 )_5': 'population density ( / km 2 )', 'administrative district_6': 'administrative district', 'jaffna_7': 'jaffna'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'population density ( / km 2 )_5': [0], 'administrative district_6': [1], 'jaffna_7': [2]} | ['administrative district', 'ds divisions', 'gn divisions', 'total area ( km 2 )', 'land area ( km 2 )', 'sri lankan tamil', 'sri lankan moors', 'sinhalese', 'indian tamil', 'other', 'total', 'population density ( / km 2 )'] | [['jaffna', '15', '435', '1025', '929', '577246', '2139', '3366', '499', '128', '583378', '569'], ['kilinochchi', '4', '95', '1279', '1205', '109528', '678', '962', '1682', '25', '112875', '88'], ['mannar', '5', '153', '1996', '1880', '80568', '16087', '1961', '394', '41', '99051', '50'], ['mullaitivu', '5', '127', '2617', '2415', '79081', '1760', '8851', '2182', '73', '91947', '35'], ['vavuniya', '4', '102', '1967', '1861', '141269', '11700', '17191', '1292', '59', '171511', '87']] |
1995 australian touring car championship | https://en.wikipedia.org/wiki/1995_Australian_Touring_Car_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16452451-2.html.csv | comparative | larry perkins won a circuit earlier than mark skaife in the 1995 australian touring car championship . | {'row_1': '1', 'row_2': '7', 'col': '3', 'col_other': '4', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'round winner', 'larry perkins'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose round winner record fuzzily matches to larry perkins .', 'tostr': 'filter_eq { all_rows ; round winner ; larry perkins }'}, 'date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; round winner ; larry perkins } ; date }', 'tointer': 'select the rows whose round winner record fuzzily matches to larry perkins . take the date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'round winner', 'mark skaife'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose round winner record fuzzily matches to mark skaife .', 'tostr': 'filter_eq { all_rows ; round winner ; mark skaife }'}, 'date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; round winner ; mark skaife } ; date }', 'tointer': 'select the rows whose round winner record fuzzily matches to mark skaife . take the date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; round winner ; larry perkins } ; date } ; hop { filter_eq { all_rows ; round winner ; mark skaife } ; date } } = true', 'tointer': 'select the rows whose round winner record fuzzily matches to larry perkins . take the date record of this row . select the rows whose round winner record fuzzily matches to mark skaife . take the date record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; round winner ; larry perkins } ; date } ; hop { filter_eq { all_rows ; round winner ; mark skaife } ; date } } = true | select the rows whose round winner record fuzzily matches to larry perkins . take the date record of this row . select the rows whose round winner record fuzzily matches to mark skaife . 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, 'round winner_7': 7, 'larry perkins_8': 8, 'date_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'round winner_11': 11, 'mark skaife_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', 'round winner_7': 'round winner', 'larry perkins_8': 'larry perkins', 'date_9': 'date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'round winner_11': 'round winner', 'mark skaife_12': 'mark skaife', 'date_13': 'date'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'round winner_7': [0], 'larry perkins_8': [0], 'date_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'round winner_11': [1], 'mark skaife_12': [1], 'date_13': [3]} | ['circuit', 'location / state', 'date', 'round winner', 'team'] | [['sandown international raceway', 'melbourne , victoria', '3 - 5 feb', 'larry perkins', 'castrol perkins motorsport'], ['symmons plains raceway', 'launceston , tasmania', '24 - 26 feb', 'john bowe', 'dick johnson racing'], ['mount panorama circuit', 'bathurst , new south wales', '10 - 12 mar', 'john bowe', 'dick johnson racing'], ['phillip island grand prix circuit', 'phillip island , victoria', '7 - 9 apr', 'glenn seton', 'glenn seton racing'], ['lakeside international raceway', 'brisbane , queensland', '21 - 23 apr', 'glenn seton', 'glenn seton racing'], ['winton motor raceway', 'benalla , victoria', '19 - 21 may', 'john bowe', 'dick johnson racing'], ['eastern creek raceway', 'sydney , new south wales', '26 - 28 may', 'mark skaife', 'gibson motor sport'], ['mallala motor sport park', 'mallala , south australia', '7 - 9 jul', 'glenn seton', 'glenn seton racing'], ['barbagallo raceway', 'perth , western australia', '14 - 16 jul', 'glenn seton', 'glenn seton racing'], ['oran park raceway', 'sydney , new south wales', '4 - 6 aug', 'john bowe', 'dick johnson racing']] |
5th united states congress | https://en.wikipedia.org/wiki/5th_United_States_Congress | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-224839-4.html.csv | count | a total of six vacators in the 5th united states congress chose to resign their seats . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'resign', 'result': '6', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'reason for change', 'resign'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose reason for change record fuzzily matches to resign .', 'tostr': 'filter_eq { all_rows ; reason for change ; resign }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; reason for change ; resign } }', 'tointer': 'select the rows whose reason for change record fuzzily matches to resign . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; reason for change ; resign } } ; 6 } = true', 'tointer': 'select the rows whose reason for change record fuzzily matches to resign . the number of such rows is 6 .'} | eq { count { filter_eq { all_rows ; reason for change ; resign } } ; 6 } = true | select the rows whose reason for change record fuzzily matches to resign . 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, 'reason for change_5': 5, 'resign_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', 'reason for change_5': 'reason for change', 'resign_6': 'resign', '6_7': '6'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'reason for change_5': [0], 'resign_6': [0], '6_7': [2]} | ['district', 'vacator', 'reason for change', 'successor', 'date successor seated'] | [['rhode island at - large', 'elisha potter ( f )', 'resigned sometime in 1797', 'thomas tillinghast ( f )', 'seated november 13 , 1797'], ['south carolina 1st', 'william l smith ( f )', 'resigned july 10 , 1797', 'thomas pinckney ( f )', 'seated november 23 , 1797'], ['massachusetts 11th', 'theophilus bradbury ( f )', 'resigned july 24 , 1797', 'bailey bartlett ( f )', 'seated november 27 , 1797'], ['new hampshire at - large', 'jeremiah smith ( f )', 'resigned july 26 , 1797', 'peleg sprague ( f )', 'seated december 15 , 1797'], ['connecticut at - large', 'james davenport ( f )', 'died august 3 , 1797', 'william edmond ( f )', 'seated november 13 , 1797'], ['pennsylvania 5th', 'george ege ( f )', 'resigned sometime in october , 1797', 'joseph hiester ( dr )', 'seated december 1 , 1797'], ['pennsylvania 4th', 'samuel sitgreaves ( f )', 'resigned sometime in 1798', 'robert brown ( dr )', 'seated december 4 , 1798'], ['north carolina 10th', 'nathan bryan ( dr )', 'died june 4 , 1798', 'richard dobbs spaight ( dr )', 'seated december 10 , 1798'], ['pennsylvania 1st', 'john swanwick ( dr )', 'died august 1 , 1798', 'robert waln ( f )', 'seated december 3 , 1798'], ['connecticut at - large', 'joshua coit ( f )', 'died september 5 , 1798', 'jonathan brace ( f )', 'seated december 3 , 1798']] |
tiburones rojos de veracruz | https://en.wikipedia.org/wiki/Tiburones_Rojos_de_Veracruz | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1193316-2.html.csv | superlative | the tiburones rojos achieved their highest rank in regular season 1 in 2004-05 . | {'scope': 'all', 'col_superlative': '3', 'row_superlative': '4', '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', 'regular season 1'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; regular season 1 }'}, 'season'], 'result': '2004 - 05', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; regular season 1 } ; season }'}, '2004 - 05'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; regular season 1 } ; season } ; 2004 - 05 } = true', 'tointer': 'select the row whose regular season 1 record of all rows is minimum . the season record of this row is 2004 - 05 .'} | eq { hop { argmin { all_rows ; regular season 1 } ; season } ; 2004 - 05 } = true | select the row whose regular season 1 record of all rows is minimum . the season record of this row is 2004 - 05 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'regular season 1_5': 5, 'season_6': 6, '2004 - 05_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'regular season 1_5': 'regular season 1', 'season_6': 'season', '2004 - 05_7': '2004 - 05'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'regular season 1_5': [0], 'season_6': [1], '2004 - 05_7': [2]} | ['season', 'pyramid level', 'regular season 1', 'playoffs 1', 'regular season 2', 'playoffs 2', 'copa mãxico', 'concacaf'] | [['2001 - 02', '2 and 1', '4th', 'champions', '11th', 'did not qualify', 'no longer played', 'did not qualify'], ['2002 - 03', '1', '18th', 'did not qualify', '7th', 'quarterfinals', 'no longer played', 'did not qualify'], ['2003 - 04', '1', '12th', 'did not qualify', '20th', 'did not qualify', 'no longer played', 'did not qualify'], ['2004 - 05', '1', '1st', 'quarterfinals', '17th', 'did not qualify', 'no longer played', 'did not qualify'], ['2005 - 06', '1', '18th', 'did not qualify', '16th', 'did not qualify', 'no longer played', 'did not qualify'], ['2006 - 07', '1', '9th', 'repechaje', '18th', 'did not qualify', 'no longer played', 'did not qualify'], ['2007 - 08', '1', '13th', 'did not qualify', '16th', 'did not qualify', 'no longer played', 'did not qualify'], ['2008 - 09', '2', '11th', 'did not qualify', '3rd', 'semifinal', 'no longer played', 'did not qualify'], ['2009 - 10', '2', '4th', 'semifinal', '15th', 'did not qualify', 'no longer played', 'did not qualify'], ['2010 - 11', '2', '5th', 'second place', '5th', 'disqualified', 'no longer played', 'did not qualify'], ['2011 - 12', '2', '8th', 'did not qualify', '13th', 'did not qualify', 'no longer played', 'did not qualify'], ['2012 - 13', '2', '12th', 'did not qualify', '4th', 'quarterfinals', '4th ( dnq )', 'did not qualify']] |
real salt lake | https://en.wikipedia.org/wiki/Real_Salt_Lake | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1053453-9.html.csv | majority | for real salt lake , all of the players are from the nation of the united states . | {'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'usa', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'nation', 'usa'], 'result': True, 'ind': 0, 'tointer': 'for the nation records of all rows , all of them fuzzily match to usa .', 'tostr': 'all_eq { all_rows ; nation ; usa } = true'} | all_eq { all_rows ; nation ; usa } = true | for the nation records of all rows , all of them fuzzily match to usa . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'nation_3': 3, 'usa_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'nation_3': 'nation', 'usa_4': 'usa'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'nation_3': [0], 'usa_4': [0]} | ['rank', 'player', 'nation', 'shutouts', 'games', 'years'] | [['1', 'nick rimando', 'usa', '72', '201', '2007 - present'], ['2', 'scott garlick', 'usa', '4', '31', '2006 - 2007'], ['2', 'dj countess', 'usa', '4', '27', '2005'], ['2', 'kyle reynish', 'usa', '4', '8', '2007 - 2012'], ['5', 'chris seitz', 'usa', '1', '7', '2007 - 2009'], ['5', 'jeff attinella', 'usa', '1', '5', '2013 - present']] |
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/1-2538117-5.html.csv | count | two of the cultural interest organizations were founded by city college of new york . | {'scope': 'all', 'criterion': 'equal', 'value': 'city college of new york', 'result': '2', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'founding university', 'city college of new york'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose founding university record fuzzily matches to city college of new york .', 'tostr': 'filter_eq { all_rows ; founding university ; city college of new york }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; founding university ; city college of new york } }', 'tointer': 'select the rows whose founding university record fuzzily matches to city college of new york . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; founding university ; city college of new york } } ; 2 } = true', 'tointer': 'select the rows whose founding university record fuzzily matches to city college of new york . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; founding university ; city college of new york } } ; 2 } = true | select the rows whose founding university record fuzzily matches to city college of new york . 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, 'founding university_5': 5, 'city college of new york_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', 'founding university_5': 'founding university', 'city college of new york_6': 'city college of new york', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'founding university_5': [0], 'city college of new york_6': [0], '2_7': [2]} | ['letters', 'organization', 'nickname', 'founding date', 'founding university', 'type'] | [['αεπ', 'alpha epsilon pi 1', 'aepi', '1913 - 11 - 07', 'new york university', 'fraternity'], ['αεφ', 'alpha epsilon phi 2', 'aephi', '1909 - 10 - 24', 'barnard college', 'sorority'], ['σαεπ', 'sigma alpha epsilon pi 3', 'sigma', '1998 - 10 - 01', 'university of california , davis', 'sorority'], ['σαμ', 'sigma alpha mu 1', 'sammy', '1909 - 11 - 26', 'city college of new york', 'fraternity'], ['σδτ', 'sigma delta tau 2', 'sdt or sig delts', '1917 - 03 - 25', 'cornell university', 'sorority'], ['τεφ', 'tau epsilon phi 1', 'tep , tau boys', '1910 - 10 - 10', 'columbia university', 'fraternity'], ['ζβτ', 'zeta beta tau 1', 'zbt', '1898 - 12 - 29', 'city college of new york', 'fraternity']] |
1974 vfl season | https://en.wikipedia.org/wiki/1974_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10869646-9.html.csv | superlative | hawthorne had the highest home team score of any of these teams . | {'scope': 'all', 'col_superlative': '2', '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', 'home team score'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; home team score }'}, 'home team'], 'result': 'hawthorn', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; home team score } ; home team }'}, 'hawthorn'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; home team score } ; home team } ; hawthorn } = true', 'tointer': 'select the row whose home team score record of all rows is maximum . the home team record of this row is hawthorn .'} | eq { hop { argmax { all_rows ; home team score } ; home team } ; hawthorn } = true | select the row whose home team score record of all rows is maximum . the home team record of this row is hawthorn . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'home team score_5': 5, 'home team_6': 6, 'hawthorn_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'home team score_5': 'home team score', 'home team_6': 'home team', 'hawthorn_7': 'hawthorn'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'home team score_5': [0], 'home team_6': [1], 'hawthorn_7': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['hawthorn', '18.15 ( 123 )', 'st kilda', '10.16 ( 76 )', 'princes park', '12630', '1 june 1974'], ['geelong', '16.12 ( 108 )', 'south melbourne', '17.7 ( 109 )', 'kardinia park', '15664', '1 june 1974'], ['footscray', '13.16 ( 94 )', 'melbourne', '8.8 ( 56 )', 'western oval', '15415', '1 june 1974'], ['north melbourne', '11.15 ( 81 )', 'essendon', '16.15 ( 111 )', 'arden street oval', '20027', '1 june 1974'], ['richmond', '9.20 ( 74 )', 'collingwood', '21.17 ( 143 )', 'mcg', '66829', '1 june 1974'], ['carlton', '16.15 ( 111 )', 'fitzroy', '7.10 ( 52 )', 'vfl park', '19906', '1 june 1974']] |
list of tallest buildings in mobile | https://en.wikipedia.org/wiki/List_of_tallest_buildings_in_Mobile | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17961233-1.html.csv | ordinal | the cathedral basilica of the immaculate conception was the 1st building to be constructed according to the list of tallest buildings in mobile . | {'row': '15', 'col': '5', 'order': '1', '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', 'year', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; year ; 1 }'}, 'name'], 'result': 'cathedral basilica of the immaculate conception', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; year ; 1 } ; name }'}, 'cathedral basilica of the immaculate conception'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; year ; 1 } ; name } ; cathedral basilica of the immaculate conception } = true', 'tointer': 'select the row whose year record of all rows is 1st minimum . the name record of this row is cathedral basilica of the immaculate conception .'} | eq { hop { nth_argmin { all_rows ; year ; 1 } ; name } ; cathedral basilica of the immaculate conception } = true | select the row whose year record of all rows is 1st minimum . the name record of this row is cathedral basilica of the immaculate conception . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'year_5': 5, '1_6': 6, 'name_7': 7, 'cathedral basilica of the immaculate conception_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', 'year_5': 'year', '1_6': '1', 'name_7': 'name', 'cathedral basilica of the immaculate conception_8': 'cathedral basilica of the immaculate conception'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'year_5': [0], '1_6': [0], 'name_7': [1], 'cathedral basilica of the immaculate conception_8': [2]} | ['rank', 'name', 'height ft ( m )', 'floors', 'year'] | [['01.0 1', 'rsa battle house tower', '745 ( 227 )', '35', '2007'], ['02.0 2', 'rsabanktrust building', '424 ( 129 )', '34', '1965'], ['03.0 3', 'renaissance riverview plaza hotel', '374 ( 114 )', '28', '1983'], ['04.0 4 =', 'mobile government plaza', '325 ( 99 )', '12', '1994'], ['05.0 4 =', 'mobile marriott', '325 ( 99 )', '20', '1979'], ['06.0 6', 'regions bank building', '236 ( 72 )', '18', '1929'], ['07.0 7', 'wachovia building', '230 ( 70 )', '16', '1947'], ['08.0 8', 'lafayette plaza hotel', '180 ( 55 )', '17', '1975'], ['09.0 9', 'providence hospital', '170 ( 52 )', '11', '1987'], ['10.0 10', 'commerce building', '160 ( 49 )', '12', '1958'], ['11.0 11', 'radisson admiral semmes hotel', '136 ( 42 )', '12', '1940'], ['12.0 12', 'van antwerp building', '120 ( 37 )', '11', '1907'], ['13.0 13', 'battle house hotel', '119 ( 36 )', '7', '1908'], ['14.0 14', 'royal st francis building', '115 ( 35 )', '7', '1908'], ['15.0 15', 'cathedral basilica of the immaculate conception', '102 ( 31 )', '2', '1850']] |
1990 foster 's cup | https://en.wikipedia.org/wiki/1990_Foster%27s_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-16387653-1.html.csv | majority | all of the games in the 1990 foster 's cup were played in february 1990 . | {'scope': 'all', 'col': '7', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'february', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'date', 'february'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , all of them fuzzily match to february .', 'tostr': 'all_eq { all_rows ; date ; february } = true'} | all_eq { all_rows ; date ; february } = true | for the date records of all rows , all of them fuzzily match to february . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, 'february_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', 'february_4': 'february'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], 'february_4': [0]} | ['home team', 'home team score', 'away team', 'away team score', 'ground', 'crowd', 'date'] | [['footscray', '10.15 ( 75 )', 'richmond', '8.6 ( 54 )', 'waverley park', '16968', 'wednesday 7 february'], ['essendon', '5.11 ( 41 )', 'west coast', '4.14 ( 38 )', 'waverley park', '6988', 'saturday 10 february'], ['fitzroy', '12.13 ( 85 )', 'st kilda', '9.13 ( 67 )', 'waverley park', '12656', 'wednesday 14 february'], ['carlton', '17.7 ( 109 )', 'collingwood', '12.10 ( 82 )', 'waverley park', '41185', 'saturday 17 february'], ['north melbourne', '10.8 ( 68 )', 'west coast 1', '8.18 ( 66 )', 'waverley park', '4554', 'wednesday 21 february']] |
lithuania in the eurovision song contest 2009 | https://en.wikipedia.org/wiki/Lithuania_in_the_Eurovision_Song_Contest_2009 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18932779-1.html.csv | unique | only one artist who represented lithuania in the 2008 eurovision song contest scored more than 90 points . | {'scope': 'all', 'row': '9', 'col': '4', 'col_other': 'n/a', 'criterion': 'greater_than', 'value': '90', 'subset': None} | {'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'points', '90'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose points record is greater than 90 .', 'tostr': 'filter_greater { all_rows ; points ; 90 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; points ; 90 } } = true', 'tointer': 'select the rows whose points record is greater than 90 . there is only one such row in the table .'} | only { filter_greater { all_rows ; points ; 90 } } = true | select the rows whose points record is greater than 90 . there is only one such row in the table . | 2 | 2 | {'only_1': 1, 'result_2': 2, 'filter_greater_0': 0, 'all_rows_3': 3, 'points_4': 4, '90_5': 5} | {'only_1': 'only', 'result_2': 'true', 'filter_greater_0': 'filter_greater', 'all_rows_3': 'all_rows', 'points_4': 'points', '90_5': '90'} | {'only_1': [2], 'result_2': [], 'filter_greater_0': [1], 'all_rows_3': [0], 'points_4': [0], '90_5': [0]} | ['draw', 'artist', 'song', 'points', 'place'] | [['1', 'jonas čepulis and skirmantė', 'uosilėli žaliasai', '49', '7'], ['2', 'alanas', 'geras jausmas', '35', '9'], ['3', 'violeta tarasovienė', 'aš būsiu šalia', '74', '3'], ['4', 'milana', 'ar tu mane matei', '30', '12'], ['5', 'vilius tarasovas', 'aš tik tavim tikiu', '64', '4'], ['6', 'augustė', 'not the best time', '41', '8'], ['7', 'darius pranckevičius and violeta valskytė', 'nelytėta viltis', '78', '2'], ['8', 'kamilė', 'no way to run', '33', '10'], ['9', 'sasha son', 'pasiklydęs žmogus', '92', '1'], ['10', 'vita rusaitytė', 'dar pabūkim drauge', '33', '10'], ['11', '69 danguje', 'meilės simfonija', '62', '5'], ['12', 'egidijus sipavičius', 'per mažai', '56', '6']] |
walter hagen | https://en.wikipedia.org/wiki/Walter_Hagen | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1507806-2.html.csv | unique | the masters tournament is the only championship in which walter hagen has no win record . | {'scope': 'all', 'row': '1', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': '0', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'wins', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose wins record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; wins ; 0 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; wins ; 0 } }', 'tointer': 'select the rows whose wins record is equal to 0 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'wins', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose wins record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; wins ; 0 }'}, 'tournament'], 'result': 'masters tournament', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; wins ; 0 } ; tournament }'}, 'masters tournament'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; wins ; 0 } ; tournament } ; masters tournament }', 'tointer': 'the tournament record of this unqiue row is masters tournament .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; wins ; 0 } } ; eq { hop { filter_eq { all_rows ; wins ; 0 } ; tournament } ; masters tournament } } = true', 'tointer': 'select the rows whose wins record is equal to 0 . there is only one such row in the table . the tournament record of this unqiue row is masters tournament .'} | and { only { filter_eq { all_rows ; wins ; 0 } } ; eq { hop { filter_eq { all_rows ; wins ; 0 } ; tournament } ; masters tournament } } = true | select the rows whose wins record is equal to 0 . there is only one such row in the table . the tournament record of this unqiue row is masters tournament . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'wins_7': 7, '0_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'tournament_9': 9, 'masters tournament_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'wins_7': 'wins', '0_8': '0', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'tournament_9': 'tournament', 'masters tournament_10': 'masters tournament'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'wins_7': [0], '0_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'tournament_9': [2], 'masters tournament_10': [3]} | ['tournament', 'wins', 'top - 5', 'top - 10', 'top - 25', 'events', 'cuts made'] | [['masters tournament', '0', '0', '0', '3', '6', '4'], ['us open', '2', '9', '16', '20', '23', '22'], ['the open championship', '4', '6', '7', '8', '10', '10'], ['pga championship', '5', '9', '10', '13', '15', '15'], ['totals', '11', '24', '33', '44', '54', '51']] |
1969 buffalo bills season | https://en.wikipedia.org/wiki/1969_Buffalo_Bills_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16351004-2.html.csv | count | four buffalo bills games in 1969 had an attendance less than 40,000 . | {'scope': 'all', 'criterion': 'less_than', 'value': '40000', 'result': '4', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'attendance', '40000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose attendance record is less than 40000 .', 'tostr': 'filter_less { all_rows ; attendance ; 40000 }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_less { all_rows ; attendance ; 40000 } }', 'tointer': 'select the rows whose attendance record is less than 40000 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_less { all_rows ; attendance ; 40000 } } ; 4 } = true', 'tointer': 'select the rows whose attendance record is less than 40000 . the number of such rows is 4 .'} | eq { count { filter_less { all_rows ; attendance ; 40000 } } ; 4 } = true | select the rows whose attendance record is less than 40000 . the number of such rows is 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_less_0': 0, 'all_rows_4': 4, 'attendance_5': 5, '40000_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_less_0': 'filter_less', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', '40000_6': '40000', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_less_0': [1], 'all_rows_4': [0], 'attendance_5': [0], '40000_6': [0], '4_7': [2]} | ['date', 'opponent', 'score', 'result', 'record', 'attendance'] | [['september 14', 'new york jets', '33 - 19', 'loss', '0 - 1', '46165'], ['september 21', 'houston oilers', '21 - 17', 'loss', '0 - 2', '40146'], ['september 28', 'denver broncos', '41 - 28', 'win', '1 - 2', '40302'], ['october 5', 'houston oilers', '28 - 14', 'loss', '1 - 3', '46485'], ['october 11', 'boston patriots', '23 - 16', 'win', '2 - 3', '46201'], ['october 19', 'oakland raiders', '50 - 21', 'loss', '2 - 4', '54418'], ['october 26', 'miami dolphins', '24 - 6', 'loss', '2 - 5', '39837'], ['november 2', 'kansas city chiefs', '29 - 7', 'loss', '2 - 6', '45844'], ['november 9', 'new york jets', '16 - 6', 'loss', '2 - 7', '62680'], ['november 16', 'miami dolphins', '28 - 3', 'win', '3 - 7', '32686'], ['november 23', 'boston patriots', '35 - 21', 'loss', '3 - 8', '25584'], ['november 30', 'cincinnati bengals', '16 - 13', 'win', '4 - 8', '35122'], ['december 7', 'kansas city chiefs', '22 - 19', 'loss', '4 - 9', '47112'], ['december 14', 'san diego chargers', '45 - 6', 'loss', '4 - 10', '47582']] |
list of england national rugby union team results 1970 - 79 | https://en.wikipedia.org/wiki/List_of_England_national_rugby_union_team_results_1970%E2%80%9379 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18178924-4.html.csv | aggregation | in the england national rugby union team results for 1973 , england lost a total of 18 points against new zealand . | {'scope': 'subset', 'col': '2', 'type': 'sum', 'result': '18', 'subset': {'col': '1', 'criterion': 'equal', 'value': 'new zealand'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opposing teams', 'new zealand'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; opposing teams ; new zealand }', 'tointer': 'select the rows whose opposing teams record fuzzily matches to new zealand .'}, 'against'], 'result': '18', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; opposing teams ; new zealand } ; against }'}, '18'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; opposing teams ; new zealand } ; against } ; 18 } = true', 'tointer': 'select the rows whose opposing teams record fuzzily matches to new zealand . the sum of the against record of these rows is 18 .'} | round_eq { sum { filter_eq { all_rows ; opposing teams ; new zealand } ; against } ; 18 } = true | select the rows whose opposing teams record fuzzily matches to new zealand . the sum of the against record of these rows is 18 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'opposing teams_5': 5, 'new zealand_6': 6, 'against_7': 7, '18_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'opposing teams_5': 'opposing teams', 'new zealand_6': 'new zealand', 'against_7': 'against', '18_8': '18'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'opposing teams_5': [0], 'new zealand_6': [0], 'against_7': [1], '18_8': [2]} | ['opposing teams', 'against', 'date', 'venue', 'status'] | [['new zealand', '9', '06 / 01 / 1973', 'twickenham , london', 'test match'], ['wales', '25', '20 / 01 / 1973', 'cardiff arms park , cardiff', 'five nations'], ['ireland', '18', '10 / 02 / 1973', 'lansdowne road , dublin', 'five nations'], ['france', '6', '24 / 02 / 1973', 'twickenham , london', 'five nations'], ['scotland', '13', '17 / 03 / 1973', 'twickenham , london', 'five nations'], ['new zealand', '9', '15 / 09 / 1973', 'eden park , auckland', 'test match'], ['australia', '3', '17 / 11 / 1973', 'twickenham , london', 'test match']] |
1978 houston oilers season | https://en.wikipedia.org/wiki/1978_Houston_Oilers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15984957-2.html.csv | ordinal | the houston oilers scored the 3rd lowest number of points during the 1978 season on oct 29 . | {'row': '9', 'col': '5', 'order': '3', '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', 'oilers points', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; oilers points ; 3 }'}, 'date'], 'result': 'oct 29', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; oilers points ; 3 } ; date }'}, 'oct 29'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; oilers points ; 3 } ; date } ; oct 29 } = true', 'tointer': 'select the row whose oilers points record of all rows is 3rd minimum . the date record of this row is oct 29 .'} | eq { hop { nth_argmin { all_rows ; oilers points ; 3 } ; date } ; oct 29 } = true | select the row whose oilers points record of all rows is 3rd minimum . the date record of this row is oct 29 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'oilers points_5': 5, '3_6': 6, 'date_7': 7, 'oct 29_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', 'oilers points_5': 'oilers points', '3_6': '3', 'date_7': 'date', 'oct 29_8': 'oct 29'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'oilers points_5': [0], '3_6': [0], 'date_7': [1], 'oct 29_8': [2]} | ['game', 'date', 'opponent', 'result', 'oilers points', 'opponents', 'oilers first downs', 'record', 'attendance'] | [['1', 'sept 3', 'atlanta falcons', 'loss', '14', '20', '13', '0 - 1', '57328'], ['2', 'sept 10', 'kansas city chiefs', 'win', '20', '17', '15', '1 - 1', '40213'], ['3', 'sept 17', 'san francisco 49ers', 'win', '20', '19', '23', '2 - 1', '46161'], ['4', 'sept 24', 'los angeles rams', 'loss', '6', '10', '10', '2 - 2', '45749'], ['5', 'oct 1', 'cleveland browns', 'win', '16', '13', '20', '3 - 2', '72776'], ['6', 'oct 8', 'oakland raiders', 'loss', '17', '21', '20', '3 - 3', '52550'], ['7', 'oct 15', 'buffalo bills', 'win', '17', '10', '17', '4 - 3', '47727'], ['8', 'oct 23', 'pittsburgh steelers', 'win', '24', '17', '22', '5 - 3', '48021'], ['9', 'oct 29', 'cincinnati bengals', 'loss', '13', '28', '15', '5 - 4', '50532'], ['10', 'nov 5', 'cleveland browns', 'win', '14', '10', '18', '6 - 4', '45827'], ['11', 'nov 12', 'new england patriots', 'win', '26', '23', '24', '7 - 4', '60356'], ['12', 'nov 20', 'miami dolphins', 'win', '35', '30', '23', '8 - 4', '50290'], ['13', 'nov 26', 'cincinnati bengals', 'win', '17', '10', '17', '9 - 4', '43245'], ['14', 'dec 3', 'pittsburgh steelers', 'loss', '3', '13', '9', '9 - 5', '54261'], ['15', 'dec 10', 'new orleans saints', 'win', '17', '12', '16', '10 - 5', '63169'], ['16', 'dec 17', 'san diego chargers', 'loss', '24', '45', '14', '10 - 6', '49554']] |
cincinnati | https://en.wikipedia.org/wiki/Cincinnati | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18522615-2.html.csv | count | a total of 6 clubs were founded after the year 2005 in cincinnati . | {'scope': 'all', 'criterion': 'greater_than_eq', 'value': '2005', 'result': '6', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater_eq', 'args': ['all_rows', 'founded', '2005'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose founded record is greater than or equal to 2005 .', 'tostr': 'filter_greater_eq { all_rows ; founded ; 2005 }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_greater_eq { all_rows ; founded ; 2005 } }', 'tointer': 'select the rows whose founded record is greater than or equal to 2005 . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_greater_eq { all_rows ; founded ; 2005 } } ; 6 } = true', 'tointer': 'select the rows whose founded record is greater than or equal to 2005 . the number of such rows is 6 .'} | eq { count { filter_greater_eq { all_rows ; founded ; 2005 } } ; 6 } = true | select the rows whose founded record is greater than or equal to 2005 . the number of such rows is 6 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_greater_eq_0': 0, 'all_rows_4': 4, 'founded_5': 5, '2005_6': 6, '6_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_greater_eq_0': 'filter_greater_eq', 'all_rows_4': 'all_rows', 'founded_5': 'founded', '2005_6': '2005', '6_7': '6'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_greater_eq_0': [1], 'all_rows_4': [0], 'founded_5': [0], '2005_6': [0], '6_7': [2]} | ['club', 'sport', 'founded', 'league', 'venue'] | [['cincinnati bengals', 'football', '1968', 'national football league , afc', 'paul brown stadium'], ['cincinnati reds', 'baseball', '1869', 'mlb , national league', 'great american ball park'], ['cincinnati cyclones', 'ice hockey', '1990', 'echl', 'us bank arena'], ['florence freedom', 'baseball', '1994', 'frontier league', 'champion window field'], ['cincinnati rollergirls', 'roller derby', '2005', "women 's flat track derby association", 'cincinnati gardens'], ['cincinnati kings', 'soccer', '2005', 'usl premier development league', 'town and country sports club'], ['cincinnati kings indoor team', 'indoor soccer', '2008', 'professional arena soccer league', 'cincinnati gardens'], ['cincinnati commandos', 'indoor football', '2010', 'ultimate indoor football league', 'cincinnati gardens'], ['cincinnati revolution', 'ultimate frisbee', '2011', 'american ultimate disc league , midwest conference', 'sheakley athletic center'], ['cincinnati saints', 'soccer', '2013', 'professional arena soccer league', 'tri - county soccerplex']] |
list of los angeles lakers broadcasters | https://en.wikipedia.org/wiki/List_of_Los_Angeles_Lakers_broadcasters | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16701360-6.html.csv | unique | channel kcal-tv is the only channel to have jim hill as a studio host for the los angeles lakers . | {'scope': 'all', 'row': '9', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': 'jim hill', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'studio host', 'jim hill'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose studio host record fuzzily matches to jim hill .', 'tostr': 'filter_eq { all_rows ; studio host ; jim hill }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; studio host ; jim hill } }', 'tointer': 'select the rows whose studio host record fuzzily matches to jim hill . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'studio host', 'jim hill'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose studio host record fuzzily matches to jim hill .', 'tostr': 'filter_eq { all_rows ; studio host ; jim hill }'}, 'channel'], 'result': 'kcal - tv', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; studio host ; jim hill } ; channel }'}, 'kcal - tv'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; studio host ; jim hill } ; channel } ; kcal - tv }', 'tointer': 'the channel record of this unqiue row is kcal - tv .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; studio host ; jim hill } } ; eq { hop { filter_eq { all_rows ; studio host ; jim hill } ; channel } ; kcal - tv } } = true', 'tointer': 'select the rows whose studio host record fuzzily matches to jim hill . there is only one such row in the table . the channel record of this unqiue row is kcal - tv .'} | and { only { filter_eq { all_rows ; studio host ; jim hill } } ; eq { hop { filter_eq { all_rows ; studio host ; jim hill } ; channel } ; kcal - tv } } = true | select the rows whose studio host record fuzzily matches to jim hill . there is only one such row in the table . the channel record of this unqiue row is kcal - tv . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'studio host_7': 7, 'jim hill_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'channel_9': 9, 'kcal - tv_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'studio host_7': 'studio host', 'jim hill_8': 'jim hill', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'channel_9': 'channel', 'kcal - tv_10': 'kcal - tv'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'studio host_7': [0], 'jim hill_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'channel_9': [2], 'kcal - tv_10': [3]} | ['channel', 'play - by - play', 'color commentator ( s )', 'studio host', 'studio analysts'] | [['kcal - tv', 'chick hearn', 'stu lantz', 'alan massengale', 'james worthy'], ['fox sports net west', 'chick hearn', 'stu lantz', 'paul sunderland', 'jack haley'], ['kcal - tv', 'paul sunderland', 'stu lantz', 'alan massengale', 'james worthy'], ['fox sports net west', 'paul sunderland', 'stu lantz', 'bill macdonald', 'jack haley or reggie theus'], ['fsn west', 'paul sunderland', 'stu lantz', 'bill macdonald', 'jack haley'], ['kcal - tv', 'joel meyers', 'stu lantz', 'alan massengale', 'james worthy'], ['fsn west', 'joel meyers', 'stu lantz', 'bill macdonald', 'jack haley'], ['fsn west', 'joel meyers', 'stu lantz', 'bill macdonald', 'jack haley or paul westphal'], ['kcal - tv', 'joel meyers', 'stu lantz', 'jim hill', 'james worthy'], ['fox sports west', 'joel meyers', 'stu lantz', 'bill macdonald', 'norm nixon or paul westphal'], ['fox sports west', 'joel meyers', 'stu lantz', 'bill macdonald', 'norm nixon']] |
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