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rail transport in argentina | https://en.wikipedia.org/wiki/Rail_transport_in_Argentina | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16456054-2.html.csv | aggregation | the total number of rail stations in argentina is 259 . | {'scope': 'all', 'col': '4', 'type': 'sum', 'result': '259', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'number of stations'], 'result': '259', 'ind': 0, 'tostr': 'sum { all_rows ; number of stations }'}, '259'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; number of stations } ; 259 } = true', 'tointer': 'the sum of the number of stations record of all rows is 259 .'} | round_eq { sum { all_rows ; number of stations } ; 259 } = true | the sum of the number of stations record of all rows is 259 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'number of stations_4': 4, '259_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'number of stations_4': 'number of stations', '259_5': '259'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'number of stations_4': [0], '259_5': [1]} | ['line', 'operator', 'line length ( kilometres )', 'number of stations', 'annual ridership ( 1998 )', 'annual ridership ( 2008 )'] | [['mitre', 'ugoms', '185 , 5', '55', '84081493', '73207048'], ['belgrano norte', 'ferrovías', '54 , 3', '22', '35931801', '45830200'], ['belgrano sur', 'ugofe', '66 , 3', '30', '16219806', '11472416'], ['roca', 'ugofe', '237 , 2', '70', '152082063', '125556026'], ['san martín', 'ugofe', '56 , 3', '19', '25581310', '46647676'], ['sarmiento', 'ugoms', '184 , 1', '40', '113218819', '118143006'], ['urquiza', 'metrovías', '29 , 9', '23', '25581310', '24212133'], ['totals :', '-', '813', '259', '451971849', '445068505']] |
list of singaporean films | https://en.wikipedia.org/wiki/List_of_Singaporean_films | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1601229-7.html.csv | comparative | the film ' last life in the universe ' grossed more money than the film ' clouds in my coffee ' . | {'row_1': '2', 'row_2': '5', 'col': '5', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'last life in the universe'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose title record fuzzily matches to last life in the universe .', 'tostr': 'filter_eq { all_rows ; title ; last life in the universe }'}, 'singapore gross'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; title ; last life in the universe } ; singapore gross }', 'tointer': 'select the rows whose title record fuzzily matches to last life in the universe . take the singapore gross record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'clouds in my coffee'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose title record fuzzily matches to clouds in my coffee .', 'tostr': 'filter_eq { all_rows ; title ; clouds in my coffee }'}, 'singapore gross'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; title ; clouds in my coffee } ; singapore gross }', 'tointer': 'select the rows whose title record fuzzily matches to clouds in my coffee . take the singapore gross record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; title ; last life in the universe } ; singapore gross } ; hop { filter_eq { all_rows ; title ; clouds in my coffee } ; singapore gross } } = true', 'tointer': 'select the rows whose title record fuzzily matches to last life in the universe . take the singapore gross record of this row . select the rows whose title record fuzzily matches to clouds in my coffee . take the singapore gross record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; title ; last life in the universe } ; singapore gross } ; hop { filter_eq { all_rows ; title ; clouds in my coffee } ; singapore gross } } = true | select the rows whose title record fuzzily matches to last life in the universe . take the singapore gross record of this row . select the rows whose title record fuzzily matches to clouds in my coffee . take the singapore gross 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, 'title_7': 7, 'last life in the universe_8': 8, 'singapore gross_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'title_11': 11, 'clouds in my coffee_12': 12, 'singapore gross_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', 'title_7': 'title', 'last life in the universe_8': 'last life in the universe', 'singapore gross_9': 'singapore gross', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'title_11': 'title', 'clouds in my coffee_12': 'clouds in my coffee', 'singapore gross_13': 'singapore gross'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'title_7': [0], 'last life in the universe_8': [0], 'singapore gross_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'title_11': [1], 'clouds in my coffee_12': [1], 'singapore gross_13': [3]} | ['date', 'title', 'director', 'production cost', 'singapore gross'] | [['2004', '2004', '2004', '2004', '2004'], ['february 2004', 'last life in the universe', 'pen - ek ratanaruang', 'us2000000', '65000'], ['march 2004', 'the eye 2', 'danny pang / oxide pang', 'us3000000', '1577000'], ['june 2004', 'the best bet ( 突然发财 )', 'jack neo', '1500000', '2664000'], ['august 2004', 'clouds in my coffee', 'gallen mei', 'us125000', '11000'], ['unreleased', 'zombie dogs', 'toh hai leong', 'na', 'na'], ['unreleased', 'outsiders', 'sam loh', 'na', 'na'], ['unreleased', 'tequila', 'jonathan lim', 'us13000', 'na']] |
2008 - 09 lega pro seconda divisione | https://en.wikipedia.org/wiki/2008%E2%80%9309_Lega_Pro_Seconda_Divisione | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17702363-3.html.csv | superlative | the highest capacity for a venue in the 2008-09 lega pro seconda divisione was for stadio san vito . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '6', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '3', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'capacity'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; capacity }'}, 'stadium'], 'result': 'stadio san vito', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; capacity } ; stadium }'}, 'stadio san vito'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; capacity } ; stadium } ; stadio san vito } = true', 'tointer': 'select the row whose capacity record of all rows is maximum . the stadium record of this row is stadio san vito .'} | eq { hop { argmax { all_rows ; capacity } ; stadium } ; stadio san vito } = true | select the row whose capacity record of all rows is maximum . the stadium record of this row is stadio san vito . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'capacity_5': 5, 'stadium_6': 6, 'stadio san vito_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'capacity_5': 'capacity', 'stadium_6': 'stadium', 'stadio san vito_7': 'stadio san vito'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'capacity_5': [0], 'stadium_6': [1], 'stadio san vito_7': [2]} | ['club', 'city', 'stadium', 'capacity', '200708 season'] | [['as andria bat', 'andria', 'stadio degli ulivi', '10500', '17th in serie c2 / c'], ['sf aversa normanna', 'aversa', 'stadio rinascita', '2000', '1st serie d / h'], ['ss barletta calcio', 'barletta', 'stadio cosimo puttilli', '5000', '2nd serie d / h'], ['ss cassino 1927', 'cassino', 'stadio gino salveti', '3700', '8th in serie c2 / c'], ['fc catanzaro', 'catanzaro', 'stadio nicola ceravolo', '13619', '10th in serie c2 / c'], ['cosenza calcio 1914', 'cosenza', 'stadio san vito', '24000', '1st serie d / i'], ['gela calcio', 'gela', 'stadio vincenzo presti', '4400', '7th in serie c2 / c'], ['fc igea virtus barcellona', 'barcellona pozzo di gotto', "stadio carlo d'alcontres", '5000', '11th in serie c2 / c'], ['ac isola liri', 'isola del liri', 'stadio conte a mangoni', '3400', '1st serie d / g'], ['ss manfredonia calcio', 'manfredonia', 'stadio miramare', '4076', '18th in serie c1 / a'], ['as melfi', 'melfi', 'stadio arturo valerio', '4500', '13th in serie c2 / c'], ['ac monopoli', 'monopoli', 'stadio vito simone veneziani', '6880', '6th in serie c2 / c'], ['as noicattaro calcio', 'noicattaro', 'stadio comunale', '2500', '12th in serie c2 / c'], ['as pescina valle del giovenco', 'avezzano', 'stadio dei marsi', '4500', '2nd in serie c2 / c'], ['ss scafatese calcio 1922', 'scafati', 'stadio comunale', '1950', '16th in serie c2 / c'], ['pol val di sangro', 'atessa', 'stadio montemarcone', '2000', '15th in serie c2 / c'], ['us vibonese calcio', 'vibo valentia', 'stadio luigi razza', '4500', '14th in serie c2 / c'], ['vigor lamezia', 'lamezia terme', "stadio guido d'ippolito", '4000', '4th in serie c2 / c']] |
athletics at the 1987 pan american games | https://en.wikipedia.org/wiki/Athletics_at_the_1987_Pan_American_Games | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10649319-3.html.csv | aggregation | for athletics at the 1987 pan american games , the teams ranked in the top 3 had a total of 85 medals . | {'scope': 'subset', 'col': '6', 'type': 'sum', 'result': '85', 'subset': {'col': '1', 'criterion': 'less_than_eq', 'value': '3'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_less_eq', 'args': ['all_rows', 'rank', '3'], 'result': None, 'ind': 0, 'tostr': 'filter_less_eq { all_rows ; rank ; 3 }', 'tointer': 'select the rows whose rank record is less than or equal to 3 .'}, 'total'], 'result': '85', 'ind': 1, 'tostr': 'sum { filter_less_eq { all_rows ; rank ; 3 } ; total }'}, '85'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_less_eq { all_rows ; rank ; 3 } ; total } ; 85 } = true', 'tointer': 'select the rows whose rank record is less than or equal to 3 . the sum of the total record of these rows is 85 .'} | round_eq { sum { filter_less_eq { all_rows ; rank ; 3 } ; total } ; 85 } = true | select the rows whose rank record is less than or equal to 3 . the sum of the total record of these rows is 85 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_less_eq_0': 0, 'all_rows_4': 4, 'rank_5': 5, '3_6': 6, 'total_7': 7, '85_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_less_eq_0': 'filter_less_eq', 'all_rows_4': 'all_rows', 'rank_5': 'rank', '3_6': '3', 'total_7': 'total', '85_8': '85'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_less_eq_0': [1], 'all_rows_4': [0], 'rank_5': [0], '3_6': [0], 'total_7': [1], '85_8': [2]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'united states ( usa )', '26', '14', '15', '55'], ['2', 'cuba ( cub )', '6', '9', '8', '23'], ['3', 'mexico ( mex )', '5', '1', '1', '7'], ['4', 'brazil ( bra )', '3', '3', '2', '8'], ['5', 'jamaica ( jam )', '2', '3', '4', '9'], ['6', 'chile ( chi )', '1', '0', '1', '2'], ['7', 'canada ( can )', '0', '7', '3', '10'], ['8', 'bahamas ( bah )', '0', '2', '3', '5'], ['9', 'dominican republic ( dom )', '0', '1', '1', '2'], ['10', 'ecuador ( ecu )', '0', '1', '0', '1'], ['10', 'costa rica ( crc )', '0', '1', '0', '1'], ['10', 'argentina ( arg )', '0', '1', '0', '1'], ['13', 'puerto rico ( pur )', '0', '0', '2', '2'], ['13', 'colombia ( col )', '0', '0', '2', '2'], ['15', 'venezuela ( ven )', '0', '0', '1', '1']] |
2005 jeux de la francophonie | https://en.wikipedia.org/wiki/2005_Jeux_de_la_Francophonie | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12402019-5.html.csv | comparative | lebanon won more total medals than the french community of belgium . | {'row_1': '1', 'row_2': '2', 'col': '6', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'lebanon'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nation record fuzzily matches to lebanon .', 'tostr': 'filter_eq { all_rows ; nation ; lebanon }'}, 'total'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; nation ; lebanon } ; total }', 'tointer': 'select the rows whose nation record fuzzily matches to lebanon . take the total record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'french community of belgium'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose nation record fuzzily matches to french community of belgium .', 'tostr': 'filter_eq { all_rows ; nation ; french community of belgium }'}, 'total'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; nation ; french community of belgium } ; total }', 'tointer': 'select the rows whose nation record fuzzily matches to french community of belgium . take the total record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; nation ; lebanon } ; total } ; hop { filter_eq { all_rows ; nation ; french community of belgium } ; total } } = true', 'tointer': 'select the rows whose nation record fuzzily matches to lebanon . take the total record of this row . select the rows whose nation record fuzzily matches to french community of belgium . take the total record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; nation ; lebanon } ; total } ; hop { filter_eq { all_rows ; nation ; french community of belgium } ; total } } = true | select the rows whose nation record fuzzily matches to lebanon . take the total record of this row . select the rows whose nation record fuzzily matches to french community of belgium . take the total 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, 'nation_7': 7, 'lebanon_8': 8, 'total_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'nation_11': 11, 'french community of belgium_12': 12, 'total_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', 'nation_7': 'nation', 'lebanon_8': 'lebanon', 'total_9': 'total', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'nation_11': 'nation', 'french community of belgium_12': 'french community of belgium', 'total_13': 'total'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'nation_7': [0], 'lebanon_8': [0], 'total_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'nation_11': [1], 'french community of belgium_12': [1], 'total_13': [3]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'lebanon', '2', '1', '0', '3'], ['2', 'french community of belgium', '1', '0', '1', '2'], ['3', 'benin', '1', '0', '0', '1'], ['3', 'canada', '1', '0', '0', '1'], ['3', 'lithuania', '1', '0', '0', '1'], ['3', 'madagascar', '1', '0', '0', '1'], ['7', 'france', '0', '1', '1', '2'], ['7', 'niger', '0', '1', '1', '2'], ['9', 'new brunswick', '0', '1', '0', '1'], ['9', 'quebec', '0', '1', '0', '1'], ['9', 'cape verde', '0', '1', '0', '1'], ['9', 'morocco', '0', '1', '0', '1'], ['13', 'burkina faso', '0', '0', '1', '1'], ['13', 'republic of the congo', '0', '0', '1', '1'], ['13', 'ivory coast', '0', '0', '1', '1'], ['13', 'macedonia', '0', '0', '1', '1']] |
1953 - 54 segunda división | https://en.wikipedia.org/wiki/1953%E2%80%9354_Segunda_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17416195-2.html.csv | aggregation | the average goals against for all the teams in the segunda division was 53 . | {'scope': 'all', 'col': '8', 'type': 'average', 'result': '53', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'goals against'], 'result': '53', 'ind': 0, 'tostr': 'avg { all_rows ; goals against }'}, '53'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; goals against } ; 53 } = true', 'tointer': 'the average of the goals against record of all rows is 53 .'} | round_eq { avg { all_rows ; goals against } ; 53 } = true | the average of the goals against record of all rows is 53 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'goals against_4': 4, '53_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'goals against_4': 'goals against', '53_5': '53'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'goals against_4': [0], '53_5': [1]} | ['position', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference'] | [['1', '30', '41', '17', '7', '6', '65', '42', '+ 23'], ['2', '30', '38', '17', '4', '9', '56', '36', '+ 20'], ['3', '30', '38', '16', '6', '8', '62', '44', '+ 18'], ['4', '30', '34', '15', '4', '11', '63', '46', '+ 17'], ['5', '30', '33', '12', '9', '9', '62', '48', '+ 14'], ['6', '30', '32', '14', '4', '12', '52', '53', '- 1'], ['7', '30', '29', '10', '9', '11', '56', '54', '+ 2'], ['8', '30', '29', '12', '5', '13', '44', '58', '- 14'], ['9', '30', '29', '11', '7', '12', '76', '59', '+ 17'], ['10', '30', '28', '12', '4', '14', '65', '55', '+ 10'], ['11', '30', '28', '9', '10', '11', '45', '61', '- 16'], ['12', '30', '28', '11', '6', '13', '38', '46', '- 8'], ['13', '30', '25', '10', '5', '15', '49', '63', '- 14'], ['14', '30', '25', '11', '3', '16', '35', '64', '- 29'], ['15', '30', '22', '8', '6', '16', '44', '56', '- 12'], ['16', '30', '21', '8', '5', '17', '42', '69', '- 27']] |
1971 african cup of champions clubs | https://en.wikipedia.org/wiki/1971_African_Cup_of_Champions_Clubs | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12423174-1.html.csv | comparative | young africans scored more total goals than secteur 6 in the 1971 african cup of champions . | {'row_1': '9', 'row_2': '8', 'col': '2', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team 1', 'young africans'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team 1 record fuzzily matches to young africans .', 'tostr': 'filter_eq { all_rows ; team 1 ; young africans }'}, 'agg'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; team 1 ; young africans } ; agg }', 'tointer': 'select the rows whose team 1 record fuzzily matches to young africans . take the agg record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team 1', 'secteur 6'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose team 1 record fuzzily matches to secteur 6 .', 'tostr': 'filter_eq { all_rows ; team 1 ; secteur 6 }'}, 'agg'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; team 1 ; secteur 6 } ; agg }', 'tointer': 'select the rows whose team 1 record fuzzily matches to secteur 6 . take the agg record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; team 1 ; young africans } ; agg } ; hop { filter_eq { all_rows ; team 1 ; secteur 6 } ; agg } } = true', 'tointer': 'select the rows whose team 1 record fuzzily matches to young africans . take the agg record of this row . select the rows whose team 1 record fuzzily matches to secteur 6 . take the agg record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; team 1 ; young africans } ; agg } ; hop { filter_eq { all_rows ; team 1 ; secteur 6 } ; agg } } = true | select the rows whose team 1 record fuzzily matches to young africans . take the agg record of this row . select the rows whose team 1 record fuzzily matches to secteur 6 . take the agg 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, 'team 1_7': 7, 'young africans_8': 8, 'agg_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'team 1_11': 11, 'secteur 6_12': 12, 'agg_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', 'team 1_7': 'team 1', 'young africans_8': 'young africans', 'agg_9': 'agg', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'team 1_11': 'team 1', 'secteur 6_12': 'secteur 6', 'agg_13': 'agg'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'team 1_7': [0], 'young africans_8': [0], 'agg_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'team 1_11': [1], 'secteur 6_12': [1], 'agg_13': [3]} | ['team 1', 'agg', 'team 2', '1st leg', '2nd leg'] | [['al - merrikh', '2 - 2 ( 5 - 4 pen )', 'tele sc asmara', '2 - 1', '0 - 1'], ['abaluhya united', '1 - 3', 'great olympics', '0 - 0', '1 - 3'], ['asc diaraf', '3 - 4', 'stade malien', '3 - 0', '0 - 4'], ['maseru united', '3 - 5', 'mmm tamatave', '1 - 2', '2 - 3'], ['as porto novo', '0 - 3', 'victoria club mokanda', '0 - 1', '0 - 2'], ['canon yaoundé', '9 - 4', 'as solidarité', '7 - 3', '2 - 1'], ['espérance', '1 - 0', 'al - ahly ( benghazi )', '0 - 0', '1 - 0'], ['secteur 6', '1 - 2', 'enugu rangers', '1 - 1', '0 - 1'], ['young africans', '2 - 0', 'lavori publici', '2 - 0', '0 - 0']] |
volleyball at the 2004 summer olympics - men 's team rosters | https://en.wikipedia.org/wiki/Volleyball_at_the_2004_Summer_Olympics_%E2%80%93_Men%27s_team_rosters | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15859432-3.html.csv | aggregation | the average weight of the men 's volleyball players at the 2004 sumer olympics was almost 88 . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '88', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'weight'], 'result': '88', 'ind': 0, 'tostr': 'avg { all_rows ; weight }'}, '88'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; weight } ; 88 } = true', 'tointer': 'the average of the weight record of all rows is 88 .'} | round_eq { avg { all_rows ; weight } ; 88 } = true | the average of the weight record of all rows is 88 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'weight_4': 4, '88_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'weight_4': 'weight', '88_5': '88'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'weight_4': [0], '88_5': [1]} | ['name', 'date of birth', 'height', 'weight', 'spike', 'block'] | [['giovane gávio', '07.09.1970', '196', '89', '340', '322'], ['andré heller', '17.12.1975', '199', '93', '339', '321'], ['mauricio lima', '27.01.1968', '184', '79', '321', '304'], ['gilberto godoy filho', '23.12.1976', '192', '85', '325', '312'], ['andré nascimento', '04.03.1979', '195', '95', '340', '320'], ['sérgio dutra santos', '15.10.1975', '184', '78', '325', '310'], ['anderson rodrigues', '21.05.1974', '190', '95', '330', '321'], ['nalbert bitencourt', '09.03.1974', '195', '82', '329', '309'], ['gustavo endres', '23.08.1975', '203', '98', '337', '325'], ['rodrigo santana', '17.04.1979', '205', '85', '350', '328'], ['ricardo garcia', '19.11.1975', '191', '89', '337', '320'], ['dante amaral', '30.09.1980', '201', '86', '345', '327']] |
united states house of representatives elections , 1816 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1816 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2668347-14.html.csv | count | 2 incumbents were re - elected during the 1816 united states house of representatives elections . | {'scope': 'all', 'criterion': 'equal', 'value': 're - elected', 'result': '2', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 're - elected'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to re - elected .', 'tostr': 'filter_eq { all_rows ; result ; re - elected }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; result ; re - elected } }', 'tointer': 'select the rows whose result record fuzzily matches to re - elected . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; result ; re - elected } } ; 2 } = true', 'tointer': 'select the rows whose result record fuzzily matches to re - elected . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; result ; re - elected } } ; 2 } = true | select the rows whose result record fuzzily matches to re - elected . 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, 'result_5': 5, 're - elected_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', 'result_5': 'result', 're - elected_6': 're - elected', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'result_5': [0], 're - elected_6': [0], '2_7': [2]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['new york 3', 'jonathan ward', 'democratic - republican', '1814', 'retired democratic - republican hold', 'caleb tompkins ( dr ) 56.8 % abraham odell ( f ) 42.8 %'], ['new york 6', 'james w wilkin', 'democratic - republican', '1815 ( special )', 're - elected', 'james w wilkin ( dr ) 55.4 % james burt ( f ) 44.6 %'], ['new york 7', 'samuel r betts', 'democratic - republican', '1814', 'retired democratic - republican hold', 'josiah hasbrouck ( dr ) 51.7 % john sudam ( f ) 48.2 %'], ['new york 10', 'hosea moffitt', 'federalist', '1812', 'retired federalist hold', 'john p cushman ( f ) 54.9 % thomas turner ( dr ) 44.9 %'], ['new york 11', 'john w taylor', 'democratic - republican', '1812', 're - elected', 'john w taylor ( dr ) 53.4 % elisha powell ( f ) 46.6 %'], ['new york 13', 'john b yates', 'democratic - republican', '1814', 'retired democratic - republican hold', 'thomas lawyer ( dr ) 54.9 % william beekman ( f ) 45.1 %'], ['new york 17', 'westel willoughby , jr', 'federalist', '1814', 'retired democratic - republican gain', 'thomas h hubbard ( dr ) 51.5 % simeon ford ( f ) 48.4 %'], ['new york 18', 'moss kent', 'federalist', '1812', 'retired federalist hold', 'david a ogden ( f ) 50.4 % ela collins ( dr ) 49.5 %']] |
1994 - 95 boston bruins season | https://en.wikipedia.org/wiki/1994%E2%80%9395_Boston_Bruins_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16189062-8.html.csv | majority | in the majority of the play off games of boston bruins against the new jersey devils in the1994 - 95 season 5 goals were scored . | {'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'new jersey devils', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'visitor', 'new jersey devils'], 'result': True, 'ind': 0, 'tointer': 'for the visitor records of all rows , most of them fuzzily match to new jersey devils .', 'tostr': 'most_eq { all_rows ; visitor ; new jersey devils } = true'} | most_eq { all_rows ; visitor ; new jersey devils } = true | for the visitor records of all rows , most of them fuzzily match to new jersey devils . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'visitor_3': 3, 'new jersey devils_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'visitor_3': 'visitor', 'new jersey devils_4': 'new jersey devils'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'visitor_3': [0], 'new jersey devils_4': [0]} | ['date', 'visitor', 'score', 'home', 'record'] | [['may 7', 'new jersey devils', '5 - 0', 'boston bruins', '0 - 1'], ['may 8', 'new jersey devils', '3 - 0', 'boston bruins', '0 - 2'], ['may 10', 'boston bruins', '3 - 2', 'new jersey devils', '1 - 2'], ['may 12', 'boston bruins', '0 - 1 ( ot )', 'new jersey devils', '1 - 3'], ['may 14', 'new jersey devils', '3 - 2', 'boston bruins', '1 - 4']] |
6th united states congress | https://en.wikipedia.org/wiki/6th_United_States_Congress | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-224840-4.html.csv | majority | the majority of the 6th united states congress vacators resigned . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'resigned', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'reason for change', 'resigned'], 'result': True, 'ind': 0, 'tointer': 'for the reason for change records of all rows , most of them fuzzily match to resigned .', 'tostr': 'most_eq { all_rows ; reason for change ; resigned } = true'} | most_eq { all_rows ; reason for change ; resigned } = true | for the reason for change records of all rows , most of them fuzzily match to resigned . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'reason for change_3': 3, 'resigned_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'reason for change_3': 'reason for change', 'resigned_4': 'resigned'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'reason for change_3': [0], 'resigned_4': [0]} | ['district', 'vacator', 'reason for change', 'successor', 'date successor seated'] | [['new york 1st', 'jonathan havens ( dr )', 'died october 25 , 1799', 'john smith ( dr )', 'february 27 , 1800'], ['connecticut at - large', 'jonathan brace ( f )', 'resigned sometime in 1800', 'john cotton smith ( f )', 'november 17 , 1800'], ['virginia 13th', 'john marshall ( f )', 'resigned june 7 , 1800 to become secretary of state', 'littleton w tazewell ( dr )', 'november 26 , 1800'], ['massachusetts 3rd', 'samuel lyman ( f )', 'resigned november 6 , 1800', 'ebenezer mattoon ( f )', 'february 2 , 1801'], ['pennsylvania 8th', 'thomas hartley ( f )', 'died december 21 , 1800', 'john stewart ( dr )', 'february 3 , 1801']] |
phoenix suns all - time roster | https://en.wikipedia.org/wiki/Phoenix_Suns_all-time_roster | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11482079-2.html.csv | unique | dennis awtrey is the only player on the phoenix suns to come from santa clara . | {'scope': 'all', 'row': '6', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': 'santa clara', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'school / country', 'santa clara'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose school / country record fuzzily matches to santa clara .', 'tostr': 'filter_eq { all_rows ; school / country ; santa clara }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; school / country ; santa clara } }', 'tointer': 'select the rows whose school / country record fuzzily matches to santa clara . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'school / country', 'santa clara'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose school / country record fuzzily matches to santa clara .', 'tostr': 'filter_eq { all_rows ; school / country ; santa clara }'}, 'player'], 'result': 'dennis awtrey', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; school / country ; santa clara } ; player }'}, 'dennis awtrey'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; school / country ; santa clara } ; player } ; dennis awtrey }', 'tointer': 'the player record of this unqiue row is dennis awtrey .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; school / country ; santa clara } } ; eq { hop { filter_eq { all_rows ; school / country ; santa clara } ; player } ; dennis awtrey } } = true', 'tointer': 'select the rows whose school / country record fuzzily matches to santa clara . there is only one such row in the table . the player record of this unqiue row is dennis awtrey .'} | and { only { filter_eq { all_rows ; school / country ; santa clara } } ; eq { hop { filter_eq { all_rows ; school / country ; santa clara } ; player } ; dennis awtrey } } = true | select the rows whose school / country record fuzzily matches to santa clara . there is only one such row in the table . the player record of this unqiue row is dennis awtrey . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'school / country_7': 7, 'santa clara_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'dennis awtrey_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'school / country_7': 'school / country', 'santa clara_8': 'santa clara', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'dennis awtrey_10': 'dennis awtrey'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'school / country_7': [0], 'santa clara_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'dennis awtrey_10': [3]} | ['player', 'pos', 'from', 'school / country', 'rebs', 'asts'] | [['alvan adams', 'c / f', '1975', 'oklahoma', '6937', '4012'], ['rafael addison', 'g / f', '1986', 'syracuse', '106', '45'], ['danny ainge', 'sg', '1992', 'byu', '454', '650'], ['louis amundson', 'pf', '2008', 'unlv', '616', '59'], ['robert archibald', 'f / c', '2003', 'illinois', '1', '1'], ['dennis awtrey', 'c', '1974', 'santa clara', '1655', '846']] |
1965 american football league draft | https://en.wikipedia.org/wiki/1965_American_Football_League_Draft | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18652198-11.html.csv | count | two defensive backs were picked in the draft from picks 81-88 . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'defensive back', 'result': '2', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', 'defensive back'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to defensive back .', 'tostr': 'filter_eq { all_rows ; position ; defensive back }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; position ; defensive back } }', 'tointer': 'select the rows whose position record fuzzily matches to defensive back . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; position ; defensive back } } ; 2 } = true', 'tointer': 'select the rows whose position record fuzzily matches to defensive back . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; position ; defensive back } } ; 2 } = true | select the rows whose position record fuzzily matches to defensive back . 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, 'position_5': 5, 'defensive back_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', 'position_5': 'position', 'defensive back_6': 'defensive back', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'position_5': [0], 'defensive back_6': [0], '2_7': [2]} | ['pick', 'team', 'player', 'position', 'college'] | [['81', 'denver broncos', 'tom vaughn', 'defensive back', 'iowa state'], ['82', 'houston oilers', 'kent mccloughan', 'cornerback', 'nebraska'], ['83', 'oakland raiders', 'bill minor', 'linebacker', 'illinois'], ['84', 'new york jets', 'jim gray', 'defensive back', 'toledo'], ['85', 'kansas city chiefs', 'al piraino', 'tackle', 'wisconsin'], ['86', 'san diego chargers', 'veran smith', 'guard', 'utah state'], ['87', 'boston patriots', 'john frechette', 'tackle', 'boston college'], ['88', 'buffalo bills', 'doug goodwin', 'running back', 'maryland eastern shore']] |
united states national rugby union team | https://en.wikipedia.org/wiki/United_States_national_rugby_union_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1145226-8.html.csv | count | 2 players on the united states national rugby union team used the san francisco venue . | {'scope': 'all', 'criterion': 'equal', 'value': 'san francisco', 'result': '2', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'san francisco'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to san francisco .', 'tostr': 'filter_eq { all_rows ; venue ; san francisco }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; venue ; san francisco } }', 'tointer': 'select the rows whose venue record fuzzily matches to san francisco . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; venue ; san francisco } } ; 2 } = true', 'tointer': 'select the rows whose venue record fuzzily matches to san francisco . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; venue ; san francisco } } ; 2 } = true | select the rows whose venue record fuzzily matches to san francisco . 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, 'venue_5': 5, 'san francisco_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', 'venue_5': 'venue', 'san francisco_6': 'san francisco', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'venue_5': [0], 'san francisco_6': [0], '2_7': [2]} | ['player', 'tries', 'conv', 'venue', 'date'] | [['dick hyland', '4', '0', 'colombes', '11 / 05 / 1924'], ['vaea anitoni', '4', '0', 'san francisco', '06 / 07 / 1996'], ['brian hightower', '4', '0', 'san francisco', '07 / 06 / 1997'], ['vaea anitoni', '4', '0', 'lisbon', '08 / 04 / 1998'], ['7 players on 3 tries', '7 players on 3 tries', '7 players on 3 tries', '7 players on 3 tries', '7 players on 3 tries']] |
1944 vfl season | https://en.wikipedia.org/wiki/1944_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10809142-18.html.csv | comparative | in the 1944 vfl season , the crowd for the game at punt road oval was 4000 less than the crowed at kardinia park . | {'row_1': '5', 'row_2': '6', 'col': '6', 'col_other': '5', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '4000', 'bigger': 'row2'}} | {'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'punt road oval'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to punt road oval .', 'tostr': 'filter_eq { all_rows ; venue ; punt road oval }'}, 'crowd'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; venue ; punt road oval } ; crowd }', 'tointer': 'select the rows whose venue record fuzzily matches to punt road oval . take the crowd record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'kardinia park'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose venue record fuzzily matches to kardinia park .', 'tostr': 'filter_eq { all_rows ; venue ; kardinia park }'}, 'crowd'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; venue ; kardinia park } ; crowd }', 'tointer': 'select the rows whose venue record fuzzily matches to kardinia park . take the crowd record of this row .'}], 'result': '-4000', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; venue ; punt road oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; kardinia park } ; crowd } }'}, '-4000'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; venue ; punt road oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; kardinia park } ; crowd } } ; -4000 } = true', 'tointer': 'select the rows whose venue record fuzzily matches to punt road oval . take the crowd record of this row . select the rows whose venue record fuzzily matches to kardinia park . take the crowd record of this row . the second record is 4000 larger than the first record .'} | eq { diff { hop { filter_eq { all_rows ; venue ; punt road oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; kardinia park } ; crowd } } ; -4000 } = true | select the rows whose venue record fuzzily matches to punt road oval . take the crowd record of this row . select the rows whose venue record fuzzily matches to kardinia park . take the crowd record of this row . the second record is 4000 larger than the first record . | 6 | 6 | {'eq_5': 5, 'result_6': 6, 'diff_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'venue_8': 8, 'punt road oval_9': 9, 'crowd_10': 10, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'venue_12': 12, 'kardinia park_13': 13, 'crowd_14': 14, '-4000_15': 15} | {'eq_5': 'eq', 'result_6': 'true', 'diff_4': 'diff', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'venue_8': 'venue', 'punt road oval_9': 'punt road oval', 'crowd_10': 'crowd', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'venue_12': 'venue', 'kardinia park_13': 'kardinia park', 'crowd_14': 'crowd', '-4000_15': '-4000'} | {'eq_5': [6], 'result_6': [], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'venue_8': [0], 'punt road oval_9': [0], 'crowd_10': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'venue_12': [1], 'kardinia park_13': [1], 'crowd_14': [3], '-4000_15': [5]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['essendon', '17.24 ( 126 )', 'south melbourne', '6.8 ( 44 )', 'windy hill', '11000', '2 september 1944'], ['collingwood', '10.8 ( 68 )', 'richmond', '15.18 ( 108 )', 'victoria park', '14000', '2 september 1944'], ['carlton', '13.10 ( 88 )', 'footscray', '12.17 ( 89 )', 'princes park', '34000', '2 september 1944'], ['st kilda', '12.10 ( 82 )', 'north melbourne', '16.15 ( 111 )', 'junction oval', '7000', '2 september 1944'], ['melbourne', '14.27 ( 111 )', 'hawthorn', '6.13 ( 49 )', 'punt road oval', '4000', '2 september 1944'], ['geelong', '10.10 ( 70 )', 'fitzroy', '15.15 ( 105 )', 'kardinia park', '8000', '2 september 1944']] |
list of list a cricket records | https://en.wikipedia.org/wiki/List_of_List_A_cricket_records | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11303072-9.html.csv | ordinal | considering the list a cricket records of most dismissals in career , the player adam gilchrist has the second highest number of catches . | {'row': '2', 'col': '5', 'order': '2', 'col_other': '3', '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', 'catches', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; catches ; 2 }'}, 'player'], 'result': 'adam gilchrist', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; catches ; 2 } ; player }'}, 'adam gilchrist'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; catches ; 2 } ; player } ; adam gilchrist } = true', 'tointer': 'select the row whose catches record of all rows is 2nd maximum . the player record of this row is adam gilchrist .'} | eq { hop { nth_argmax { all_rows ; catches ; 2 } ; player } ; adam gilchrist } = true | select the row whose catches record of all rows is 2nd maximum . the player record of this row is adam gilchrist . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'catches_5': 5, '2_6': 6, 'player_7': 7, 'adam gilchrist_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', 'catches_5': 'catches', '2_6': '2', 'player_7': 'player', 'adam gilchrist_8': 'adam gilchrist'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'catches_5': [0], '2_6': [0], 'player_7': [1], 'adam gilchrist_8': [2]} | ['rank', 'dismissals', 'player', 'nationality', 'catches', 'stumpings', 'career span'] | [['1', '661', 'steve rhodes', 'england', '532', '129', '1984 - 2004'], ['2', '591', 'adam gilchrist', 'australia', '526', '65', '1992 - 2010'], ['3', '563', 'jack russell', 'england', '465', '98', '1982 - 2004'], ['4', '541', 'kumar sangakkara', 'sri lanka', '435', '106', '1997 -'], ['5', '527', 'warren hegg', 'england', '466', '61', '1987 - 2005'], ['5', '520', 'paul nixon', 'england', '421', '99', '1987 - 2011'], ['6', '515', 'mark boucher', 'south africa', '484', '31', '1995 - 2011'], ['7', '490', 'alec stewart', 'england', '442', '48', '1995 - 2011'], ['8', '476', 'moin khan', 'pakistan', '337', '139', '1995 - 2011'], ['9', '447', 'david bairstow', 'england', '411', '36', '1995 - 2011'], ['10', '436', 'richard blakey', 'south africa', '375', '61', '1995 - 2011']] |
australia fed cup team | https://en.wikipedia.org/wiki/Australia_Fed_Cup_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11233323-12.html.csv | ordinal | the australian fed cup team had the best play on carpet with a 1-0 record . | {'row': '3', 'col': '5', 'order': '1', 'col_other': 'n/a', 'max_or_min': 'max_to_min', 'value_mentioned': 'yes', 'scope': 'all', 'subset': None} | {'func': 'eq', 'args': [{'func': 'nth_max', 'args': ['all_rows', 'carpet', '1'], 'result': '1 - 0', 'ind': 0, 'tostr': 'nth_max { all_rows ; carpet ; 1 }', 'tointer': 'the 1st maximum carpet record of all rows is 1 - 0 .'}, '1 - 0'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_max { all_rows ; carpet ; 1 } ; 1 - 0 } = true', 'tointer': 'the 1st maximum carpet record of all rows is 1 - 0 .'} | eq { nth_max { all_rows ; carpet ; 1 } ; 1 - 0 } = true | the 1st maximum carpet record of all rows is 1 - 0 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'nth_max_0': 0, 'all_rows_3': 3, 'carpet_4': 4, '1_5': 5, '1 - 0_6': 6} | {'eq_1': 'eq', 'result_2': 'true', 'nth_max_0': 'nth_max', 'all_rows_3': 'all_rows', 'carpet_4': 'carpet', '1_5': '1', '1 - 0_6': '1 - 0'} | {'eq_1': [2], 'result_2': [], 'nth_max_0': [1], 'all_rows_3': [0], 'carpet_4': [0], '1_5': [0], '1 - 0_6': [1]} | ['record', 'hard', 'clay', 'grass', 'carpet'] | [['2 - 0', '0 - 0', '1 - 0', '1 - 0', '0 - 0'], ['2 - 0', '2 - 0', '0 - 0', '0 - 0', '0 - 0'], ['2 - 0', '1 - 0', '0 - 0', '0 - 0', '1 - 0'], ['2 - 0', '0 - 0', '2 - 0', '0 - 0', '0 - 0'], ['2 - 1', '1 - 1', '1 - 0', '0 - 0', '0 - 0'], ['2 - 3', '0 - 0', '1 - 3', '1 - 0', '0 - 0'], ['1 - 0', '1 - 0', '0 - 0', '0 - 0', '0 - 0'], ['1 - 0', '0 - 0', '1 - 0', '0 - 0', '0 - 0'], ['1 - 0', '0 - 0', '0 - 0', '1 - 0', '0 - 0'], ['1 - 1', '1 - 1', '0 - 0', '0 - 0', '0 - 0'], ['1 - 2', '0 - 1', '1 - 1', '0 - 0', '0 - 0']] |
1983 world judo championships | https://en.wikipedia.org/wiki/1983_World_Judo_Championships | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15807776-2.html.csv | comparative | the united states and poland both did not receive any gold or silver medals and only one bronze medal each . | {'row_1': '13', 'row_2': '14', 'col': '5', 'col_other': '2', 'relation': 'equal', 'record_mentioned': 'yes', 'diff_result': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'united states'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nation record fuzzily matches to united states .', 'tostr': 'filter_eq { all_rows ; nation ; united states }'}, 'bronze'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; nation ; united states } ; bronze }', 'tointer': 'select the rows whose nation record fuzzily matches to united states . take the bronze record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'poland'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose nation record fuzzily matches to poland .', 'tostr': 'filter_eq { all_rows ; nation ; poland }'}, 'bronze'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; nation ; poland } ; bronze }', 'tointer': 'select the rows whose nation record fuzzily matches to poland . take the bronze record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { all_rows ; nation ; united states } ; bronze } ; hop { filter_eq { all_rows ; nation ; poland } ; bronze } }', 'tointer': 'select the rows whose nation record fuzzily matches to united states . take the bronze record of this row . select the rows whose nation record fuzzily matches to poland . take the bronze record of this row . the first record is equal to the second record .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'united states'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nation record fuzzily matches to united states .', 'tostr': 'filter_eq { all_rows ; nation ; united states }'}, 'bronze'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; nation ; united states } ; bronze }', 'tointer': 'select the rows whose nation record fuzzily matches to united states . take the bronze record of this row .'}, '1'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; nation ; united states } ; bronze } ; 1 }', 'tointer': 'the bronze record of the first row is 1 .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'poland'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose nation record fuzzily matches to poland .', 'tostr': 'filter_eq { all_rows ; nation ; poland }'}, 'bronze'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; nation ; poland } ; bronze }', 'tointer': 'select the rows whose nation record fuzzily matches to poland . take the bronze record of this row .'}, '1'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; nation ; poland } ; bronze } ; 1 }', 'tointer': 'the bronze record of the second row is 1 .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; nation ; united states } ; bronze } ; 1 } ; eq { hop { filter_eq { all_rows ; nation ; poland } ; bronze } ; 1 } }', 'tointer': 'the bronze record of the first row is 1 . the bronze record of the second row is 1 .'}], 'result': True, 'ind': 8, 'tostr': 'and { eq { hop { filter_eq { all_rows ; nation ; united states } ; bronze } ; hop { filter_eq { all_rows ; nation ; poland } ; bronze } } ; and { eq { hop { filter_eq { all_rows ; nation ; united states } ; bronze } ; 1 } ; eq { hop { filter_eq { all_rows ; nation ; poland } ; bronze } ; 1 } } } = true', 'tointer': 'select the rows whose nation record fuzzily matches to united states . take the bronze record of this row . select the rows whose nation record fuzzily matches to poland . take the bronze record of this row . the first record is equal to the second record . the bronze record of the first row is 1 . the bronze record of the second row is 1 .'} | and { eq { hop { filter_eq { all_rows ; nation ; united states } ; bronze } ; hop { filter_eq { all_rows ; nation ; poland } ; bronze } } ; and { eq { hop { filter_eq { all_rows ; nation ; united states } ; bronze } ; 1 } ; eq { hop { filter_eq { all_rows ; nation ; poland } ; bronze } ; 1 } } } = true | select the rows whose nation record fuzzily matches to united states . take the bronze record of this row . select the rows whose nation record fuzzily matches to poland . take the bronze record of this row . the first record is equal to the second record . the bronze record of the first row is 1 . the bronze record of the second row is 1 . | 13 | 9 | {'and_8': 8, 'result_9': 9, 'eq_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'nation_11': 11, 'united states_12': 12, 'bronze_13': 13, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'nation_15': 15, 'poland_16': 16, 'bronze_17': 17, 'and_7': 7, 'eq_5': 5, '1_18': 18, 'eq_6': 6, '1_19': 19} | {'and_8': 'and', 'result_9': 'true', 'eq_4': 'eq', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'nation_11': 'nation', 'united states_12': 'united states', 'bronze_13': 'bronze', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'nation_15': 'nation', 'poland_16': 'poland', 'bronze_17': 'bronze', 'and_7': 'and', 'eq_5': 'eq', '1_18': '1', 'eq_6': 'eq', '1_19': '1'} | {'and_8': [9], 'result_9': [], 'eq_4': [8], 'num_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'nation_11': [0], 'united states_12': [0], 'bronze_13': [2], 'num_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'nation_15': [1], 'poland_16': [1], 'bronze_17': [3], 'and_7': [8], 'eq_5': [7], '1_18': [5], 'eq_6': [7], '1_19': [6]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'japan', '4', '1', '2', '7'], ['2', 'soviet union', '2', '1', '2', '5'], ['3', 'east germany', '2', '0', '2', '4'], ['4', 'italy', '0', '1', '1', '2'], ['4', 'hungary', '0', '1', '1', '2'], ['6', 'france', '0', '1', '0', '1'], ['6', 'czech republic', '0', '1', '0', '1'], ['6', 'great britain', '0', '1', '0', '1'], ['6', 'netherlands', '0', '1', '0', '1'], ['10', 'germany', '0', '0', '2', '2'], ['10', 'belgium', '0', '0', '2', '2'], ['10', 'romania', '0', '0', '2', '2'], ['13', 'united states', '0', '0', '1', '1'], ['13', 'poland', '0', '0', '1', '1']] |
zhang chunhui | https://en.wikipedia.org/wiki/Zhang_Chunhui | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11409274-2.html.csv | count | of the competitions zhang chunhui participated in , four of them were in hong kong . | {'scope': 'all', 'criterion': 'equal', 'value': 'hong kong', 'result': '4', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'hong kong'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to hong kong .', 'tostr': 'filter_eq { all_rows ; venue ; hong kong }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; venue ; hong kong } }', 'tointer': 'select the rows whose venue record fuzzily matches to hong kong . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; venue ; hong kong } } ; 4 } = true', 'tointer': 'select the rows whose venue record fuzzily matches to hong kong . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; venue ; hong kong } } ; 4 } = true | select the rows whose venue record fuzzily matches to hong kong . 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, 'venue_5': 5, 'hong kong_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', 'venue_5': 'venue', 'hong kong_6': 'hong kong', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'venue_5': [0], 'hong kong_6': [0], '4_7': [2]} | ['date', 'venue', 'result', 'goals', 'competition'] | [['14 january 2009', 'hong kong stadium , hong kong', '2 - 1', '0', 'friendly'], ['21 january 2009', 'hong kong stadium , hong kong', '1 - 3', '0', '2011 afc asian cup qualification'], ['28 january 2009', "ali muhesen stadium , sana'a , yemen", '0 - 1', '0', '2011 afc asian cup qualification'], ['27 august 2009', 'world games stadium , kaohsiung , taiwan', '12 - 0', '0', '2010 east asian football championship semi - final'], ['9 october 2009', 'outsourcing stadium , shizuoka , japan', '0 - 6', '0', '2011 afc asian cup qualification'], ['18 november 2009', 'hong kong stadium , hong kong', '0 - 4', '0', '2011 afc asian cup qualification'], ['7 february 2010', 'olympic stadium , tokyo , japan', '0 - 5', '0', '2010 east asian football championship'], ['17 november 2010', 'hong kong stadium , hong kong', '0 - 7', '0', 'friendly']] |
ingo schultz | https://en.wikipedia.org/wiki/Ingo_Schultz | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15186827-1.html.csv | count | ingo schultz had most of his success in 2002 , placing four times that year . | {'scope': 'all', 'criterion': 'equal', 'value': '2002', 'result': '4', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'year', '2002'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record is equal to 2002 .', 'tostr': 'filter_eq { all_rows ; year ; 2002 }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; year ; 2002 } }', 'tointer': 'select the rows whose year record is equal to 2002 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; year ; 2002 } } ; 4 } = true', 'tointer': 'select the rows whose year record is equal to 2002 . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; year ; 2002 } } ; 4 } = true | select the rows whose year record is equal to 2002 . the number of such rows is 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'year_5': 5, '2002_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'year_5': 'year', '2002_6': '2002', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'year_5': [0], '2002_6': [0], '4_7': [2]} | ['year', 'tournament', 'venue', 'result', 'extra'] | [['2000', 'european indoor championships', 'ghent , belgium', '2nd', '4x400 m relay'], ['2001', 'world championships', 'edmonton , canada', '2nd', '400 m'], ['2002', 'european championships', 'munich , germany', '1st', '400 m'], ['2002', 'european championships', 'munich , germany', '7th', '4x400 m relay'], ['2002', 'world cup', 'madrid , spain', '2nd', '400 m'], ['2002', 'world cup', 'madrid , spain', '7th', '4x400 m relay'], ['2004', 'olympic games', 'athens , greece', '7th', '4x400 m relay']] |
2008 - 09 philadelphia 76ers season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Philadelphia_76ers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17323042-7.html.csv | majority | all games of the philadelphia 76ers ' in the 2008 - 09 season were played in the month of january . | {'scope': 'all', 'col': '2', 'most_or_all': 'all', 'criterion': 'fuzzily_match', 'value': 'january', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'date', 'january'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , all of them fuzzily match to january .', 'tostr': 'all_eq { all_rows ; date ; january } = true'} | all_eq { all_rows ; date ; january } = true | for the date records of all rows , all of them fuzzily match to january . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, 'january_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', 'january_4': 'january'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], 'january_4': [0]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['32', 'january 2', 'dallas', 'l 86 - 96 ( ot )', 'andre iguodala ( 22 )', 'andre miller ( 11 )', 'andre iguodala ( 5 )', 'american airlines center 20327', '13 - 19'], ['33', 'january 3', 'san antonio', 'l 106 - 108 ( ot )', 'andre miller ( 28 )', 'andre iguodala ( 8 )', 'andre iguodala ( 8 )', 'at & t center 18797', '13 - 20'], ['34', 'january 6', 'houston', 'w 104 - 96 ( ot )', 'andre iguodala ( 28 )', 'marreese speights ( 8 )', 'andre miller , louis williams ( 8 )', 'wachovia center 14858', '14 - 20'], ['35', 'january 7', 'milwaukee', 'w 110 - 105 ( ot )', 'andre miller ( 28 )', 'andre miller ( 9 )', 'andre iguodala ( 7 )', 'bradley center 13381', '15 - 20'], ['36', 'january 9', 'charlotte', 'w 93 - 87 ( ot )', 'andre miller ( 22 )', 'samuel dalembert ( 9 )', 'andre iguodala ( 7 )', 'wachovia center 14235', '16 - 20'], ['37', 'january 11', 'atlanta', 'w 109 - 94 ( ot )', 'andre iguodala ( 27 )', 'thaddeus young ( 9 )', 'andre iguodala ( 9 )', 'philips arena 15079', '17 - 20'], ['38', 'january 14', 'portland', 'w 100 - 79 ( ot )', 'andre iguodala ( 29 )', 'samuel dalembert ( 9 )', 'louis williams , andre iguodala , andre miller ( 6 )', 'wachovia center 14561', '18 - 20'], ['39', 'january 16', 'san antonio', 'w 109 - 87 ( ot )', 'thaddeus young ( 27 )', 'samuel dalembert ( 12 )', 'andre iguodala ( 8 )', 'wachovia center 18739', '19 - 20'], ['40', 'january 17', 'new york', 'w 107 - 97 ( ot )', 'andre iguodala ( 28 )', 'andre iguodala , thaddeus young ( 10 )', 'andre miller ( 8 )', 'madison square garden 19408', '20 - 20'], ['41', 'january 19', 'dallas', 'l 93 - 95 ( ot )', 'louis williams ( 25 )', 'andre iguodala ( 12 )', 'andre miller ( 7 )', 'wachovia center 14503', '20 - 21'], ['42', 'january 24', 'new york', 'w 116 - 110 ( ot )', 'andre iguodala ( 24 )', 'samuel dalembert ( 17 )', 'andre iguodala ( 6 )', 'wachovia center 19239', '21 - 21'], ['43', 'january 26', 'new orleans', 'l 86 - 101 ( ot )', 'thaddeus young ( 22 )', 'samuel dalembert ( 12 )', 'andre iguodala ( 7 )', 'new orleans arena 16131', '21 - 22'], ['44', 'january 28', 'houston', 'w 95 - 93 ( ot )', 'andre iguodala ( 20 )', 'samuel dalembert ( 13 )', 'andre miller ( 7 )', 'toyota center 15544', '22 - 22'], ['45', 'january 30', 'washington', 'w 104 - 94 ( ot )', 'andre iguodala , willie green ( 20 )', 'thaddeus young ( 9 )', 'andre miller ( 9 )', 'wachovia center 15528', '23 - 22'], ['46', 'january 31', 'new jersey', 'l 83 - 85 ( ot )', 'andre miller ( 19 )', 'elton brand ( 9 )', 'andre miller ( 7 )', 'wachovia center 17783', '23 - 23']] |
1963 baltimore colts season | https://en.wikipedia.org/wiki/1963_Baltimore_Colts_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14984103-1.html.csv | unique | the colts only score 40 or more points 1 time during the season . | {'scope': 'all', 'row': '13', 'col': '4', 'col_other': 'n/a', 'criterion': 'greater_than', 'value': '40', 'subset': None} | {'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'result', '40'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record is greater than 40 .', 'tostr': 'filter_greater { all_rows ; result ; 40 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; result ; 40 } } = true', 'tointer': 'select the rows whose result record is greater than 40 . there is only one such row in the table .'} | only { filter_greater { all_rows ; result ; 40 } } = true | select the rows whose result record is greater than 40 . 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, 'result_4': 4, '40_5': 5} | {'only_1': 'only', 'result_2': 'true', 'filter_greater_0': 'filter_greater', 'all_rows_3': 'all_rows', 'result_4': 'result', '40_5': '40'} | {'only_1': [2], 'result_2': [], 'filter_greater_0': [1], 'all_rows_3': [0], 'result_4': [0], '40_5': [0]} | ['week', 'date', 'opponent', 'result', 'record', 'game site', 'attendance'] | [['1', 'september 15 , 1963', 'new york giants', 'l 28 - 37', '0 - 1', 'memorial stadium', '60029'], ['2', 'september 22 , 1963', 'san francisco 49ers', 'w 20 - 14', '1 - 1', 'kezar stadium', '31006'], ['3', 'september 29 , 1963', 'green bay packers', 'l 20 - 31', '1 - 2', 'lambeau field', '42327'], ['4', 'october 6 , 1963', 'chicago bears', 'l 3 - 10', '1 - 3', 'wrigley field', '48998'], ['5', 'october 13 , 1963', 'san francisco 49ers', 'w 20 - 3', '2 - 3', 'memorial stadium', '56962'], ['6', 'october 20 , 1963', 'detroit lions', 'w 25 - 21', '3 - 3', 'tiger stadium', '51901'], ['7', 'october 27 , 1963', 'green bay packers', 'l 20 - 34', '3 - 4', 'memorial stadium', '60065'], ['8', 'november 3 , 1963', 'chicago bears', 'l 7 - 17', '3 - 5', 'memorial stadium', '60065'], ['9', 'november 10 , 1963', 'detroit lions', 'w 24 - 21', '4 - 5', 'memorial stadium', '59758'], ['10', 'november 17 , 1963', 'minnesota vikings', 'w 37 - 34', '5 - 5', 'metropolitan stadium', '33136'], ['11', 'november 24 , 1963', 'los angeles rams', 'l 16 - 17', '5 - 6', 'los angeles memorial coliseum', '48555'], ['12', 'december 1 , 1963', 'washington redskins', 'w 36 - 20', '6 - 6', 'rfk stadium', '44006'], ['13', 'december 8 , 1963', 'minnesota vikings', 'w 41 - 10', '7 - 6', 'memorial stadium', '54122']] |
1951 - 52 segunda división | https://en.wikipedia.org/wiki/1951%E2%80%9352_Segunda_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17298923-2.html.csv | aggregation | the clubs in the 1951 - 52 segunda división recorded a combined total of 812 goals for . | {'scope': 'all', 'col': '7', 'type': 'sum', 'result': '812', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'goals for'], 'result': '812', 'ind': 0, 'tostr': 'sum { all_rows ; goals for }'}, '812'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; goals for } ; 812 } = true', 'tointer': 'the sum of the goals for record of all rows is 812 .'} | round_eq { sum { all_rows ; goals for } ; 812 } = true | the sum of the goals for record of all rows is 812 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'goals for_4': 4, '812_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'goals for_4': 'goals for', '812_5': '812'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'goals for_4': [0], '812_5': [1]} | ['position', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference'] | [['1', '30', '39', '16', '7', '7', '66', '29', '+ 37'], ['2', '30', '36', '15', '6', '9', '48', '40', '+ 8'], ['3', '30', '33', '11', '11', '8', '55', '44', '+ 11'], ['4', '30', '33', '13', '7', '10', '58', '44', '+ 14'], ['5', '30', '33', '13', '7', '10', '61', '32', '+ 29'], ['6', '30', '33', '12', '9', '9', '49', '41', '+ 8'], ['7', '30', '32', '14', '4', '12', '41', '52', '- 11'], ['8', '30', '32', '13', '6', '11', '55', '45', '+ 10'], ['9', '30', '30', '10', '10', '10', '51', '50', '+ 1'], ['10', '30', '29', '12', '5', '13', '49', '60', '- 11'], ['11', '30', '29', '13', '3', '14', '56', '64', '- 8'], ['12', '30', '28', '12', '4', '14', '51', '57', '- 6'], ['13', '30', '28', '9', '10', '11', '51', '71', '- 20'], ['14', '30', '24', '9', '6', '15', '40', '56', '- 16'], ['15', '30', '21', '7', '7', '16', '49', '64', '- 15'], ['16', '30', '20', '5', '10', '15', '32', '63', '- 31']] |
1983 - 84 liverpool f.c. season | https://en.wikipedia.org/wiki/1983%E2%80%9384_Liverpool_F.C._season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18269885-4.html.csv | count | in the 1983 - 84 liverpool f.c. season , among the games played against fulham , 2 of them had result 1-1 . | {'scope': 'subset', 'criterion': 'equal', 'value': '1-1', 'result': '2', 'col': '4', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'fulham'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponents', 'fulham'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; opponents ; fulham }', 'tointer': 'select the rows whose opponents record fuzzily matches to fulham .'}, 'result', '1-1'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponents record fuzzily matches to fulham . among these rows , select the rows whose result record fuzzily matches to 1-1 .', 'tostr': 'filter_eq { filter_eq { all_rows ; opponents ; fulham } ; result ; 1-1 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; opponents ; fulham } ; result ; 1-1 } }', 'tointer': 'select the rows whose opponents record fuzzily matches to fulham . among these rows , select the rows whose result record fuzzily matches to 1-1 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; opponents ; fulham } ; result ; 1-1 } } ; 2 } = true', 'tointer': 'select the rows whose opponents record fuzzily matches to fulham . among these rows , select the rows whose result record fuzzily matches to 1-1 . the number of such rows is 2 .'} | eq { count { filter_eq { filter_eq { all_rows ; opponents ; fulham } ; result ; 1-1 } } ; 2 } = true | select the rows whose opponents record fuzzily matches to fulham . among these rows , select the rows whose result record fuzzily matches to 1-1 . 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, 'opponents_6': 6, 'fulham_7': 7, 'result_8': 8, '1-1_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', 'opponents_6': 'opponents', 'fulham_7': 'fulham', 'result_8': 'result', '1-1_9': '1-1', '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], 'opponents_6': [0], 'fulham_7': [0], 'result_8': [1], '1-1_9': [1], '2_10': [3]} | ['date', 'opponents', 'venue', 'result', 'attendance', 'report 1'] | [['05 - oct - 83', 'brentford', 'a', '4 - 1', '17859', 'report'], ['25 - oct - 83', 'brentford', 'h', '4 - 0', '9902', 'report'], ['08 - nov - 83', 'fulham', 'a', '1 - 1', '20142', 'report'], ['22 - nov - 83', 'fulham', 'h', '1 - 1', '15783', 'report'], ['29 - nov - 83', 'fulham', 'a', '1 - 0', '20905', 'report'], ['20 - dec - 83', 'birmingham city', 'a', '1 - 1', '17405', 'report'], ['22 - dec - 83', 'birmingham city', 'h', '3 - 0', '11638', 'report'], ['17 - jan - 84', 'sheffield wednesday', 'a', '2 - 2', '49357', 'report'], ['25 - jan - 84', 'sheffield wednesday', 'h', '3 - 0', '40485', 'report'], ['07 - feb - 84', 'walsall', 'h', '2 - 2', '31073', 'report'], ['14 - feb - 84', 'walsall', 'a', '2 - 0', '19591', 'report']] |
2008 - 09 washington wizards season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Washington_Wizards_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17311812-7.html.csv | majority | caron butler recorded the majority of high assists performances in the 2008 - 09 washington wizards season . | {'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'caron butler', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'high assists', 'caron butler'], 'result': True, 'ind': 0, 'tointer': 'for the high assists records of all rows , most of them fuzzily match to caron butler .', 'tostr': 'most_eq { all_rows ; high assists ; caron butler } = true'} | most_eq { all_rows ; high assists ; caron butler } = true | for the high assists records of all rows , most of them fuzzily match to caron butler . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'high assists_3': 3, 'caron butler_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'high assists_3': 'high assists', 'caron butler_4': 'caron butler'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'high assists_3': [0], 'caron butler_4': [0]} | ['game', 'date', 'team', 'score', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['31', 'january 2', 'boston', 'l 83 - 108 ( ot )', 'antawn jamison ( 9 )', 'caron butler ( 5 )', 'td banknorth garden 18624', '6 - 25'], ['32', 'january 4', 'cleveland', 'w 80 - 77 ( ot )', 'antawn jamison ( 13 )', 'andray blatche ( 4 )', 'verizon center 20173', '7 - 25'], ['33', 'january 6', 'orlando', 'l 80 - 89 ( ot )', 'antawn jamison ( 9 )', 'caron butler , mike james ( 5 )', 'amway arena 16011', '7 - 26'], ['34', 'january 7', 'toronto', 'l 93 - 99 ( ot )', 'antawn jamison ( 7 )', 'caron butler , javaris crittenton ( 6 )', 'verizon center 13864', '7 - 27'], ['35', 'january 9', 'chicago', 'l 86 - 98 ( ot )', 'antawn jamison ( 11 )', 'caron butler ( 6 )', 'united center 20125', '7 - 28'], ['36', 'january 10', 'charlotte', 'l 89 - 92 ( ot )', 'andray blatche ( 10 )', 'andray blatche ( 4 )', 'verizon center 20173', '7 - 29'], ['37', 'january 12', 'milwaukee', 'l 91 - 97 ( ot )', 'dominic mcguire ( 10 )', 'dominic mcguire ( 5 )', 'verizon center 13510', '7 - 30'], ['38', 'january 14', 'new york', 'l 122 - 128 ( ot )', 'antawn jamison ( 7 )', 'mike james ( 5 )', 'madison square garden 18020', '7 - 31'], ['39', 'january 16', 'new york', 'w 96 - 89 ( ot )', 'andray blatche ( 11 )', 'caron butler ( 7 )', 'verizon center 17526', '8 - 31'], ['40', 'january 19', 'golden state', 'l 98 - 119 ( ot )', 'dominic mcguire ( 11 )', 'dominic mcguire ( 6 )', 'oracle arena 19244', '8 - 32'], ['41', 'january 21', 'sacramento', 'w 110 - 107 ( ot )', 'dominic mcguire ( 12 )', 'caron butler ( 5 )', 'arco arena 10821', '9 - 32'], ['42', 'january 22', 'la lakers', 'l 97 - 117 ( ot )', 'javale mcgee ( 9 )', 'caron butler , mike james ( 6 )', 'staples center 18997', '9 - 33'], ['43', 'january 24', 'portland', 'l 87 - 100 ( ot )', 'caron butler ( 10 )', 'mike james ( 7 )', 'rose garden 20566', '9 - 34'], ['44', 'january 26', 'phoenix', 'l 87 - 103 ( ot )', 'antawn jamison ( 13 )', 'dominic mcguire ( 7 )', 'verizon center 17344', '9 - 35'], ['45', 'january 28', 'miami', 'l 71 - 93 ( ot )', 'antawn jamison ( 12 )', 'caron butler ( 6 )', 'american airlines arena 16424', '9 - 36'], ['46', 'january 30', 'philadelphia', 'l 94 - 104 ( ot )', 'antawn jamison ( 15 )', 'javaris crittenton ( 7 )', 'wachovia center 15528', '9 - 37'], ['47', 'january 31', 'la clippers', 'w 106 - 94 ( ot )', 'caron butler ( 13 )', 'caron butler ( 7 )', 'verizon center 18227', '10 - 37']] |
2000 belarusian premier league | https://en.wikipedia.org/wiki/2000_Belarusian_Premier_League | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14747235-1.html.csv | comparative | more people can fit in the stadium in minsk than the stadium that is located in lida . | {'row_1': '10', 'row_2': '13', 'col': '4', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'minsk'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record fuzzily matches to minsk .', 'tostr': 'filter_eq { all_rows ; location ; minsk }'}, 'capacity'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; location ; minsk } ; capacity }', 'tointer': 'select the rows whose location record fuzzily matches to minsk . take the capacity record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'lida'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose location record fuzzily matches to lida .', 'tostr': 'filter_eq { all_rows ; location ; lida }'}, 'capacity'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; location ; lida } ; capacity }', 'tointer': 'select the rows whose location record fuzzily matches to lida . take the capacity record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; location ; minsk } ; capacity } ; hop { filter_eq { all_rows ; location ; lida } ; capacity } } = true', 'tointer': 'select the rows whose location record fuzzily matches to minsk . take the capacity record of this row . select the rows whose location record fuzzily matches to lida . take the capacity record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; location ; minsk } ; capacity } ; hop { filter_eq { all_rows ; location ; lida } ; capacity } } = true | select the rows whose location record fuzzily matches to minsk . take the capacity record of this row . select the rows whose location record fuzzily matches to lida . take the capacity 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, 'location_7': 7, 'minsk_8': 8, 'capacity_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'location_11': 11, 'lida_12': 12, 'capacity_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', 'location_7': 'location', 'minsk_8': 'minsk', 'capacity_9': 'capacity', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'location_11': 'location', 'lida_12': 'lida', 'capacity_13': 'capacity'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'location_7': [0], 'minsk_8': [0], 'capacity_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'location_11': [1], 'lida_12': [1], 'capacity_13': [3]} | ['team', 'location', 'venue', 'capacity', 'position in 1999'] | [['bate', 'borisov', 'city stadium , borisov', '5500', '1'], ['slavia', 'mozyr', 'yunost , mozyr', '5500', '2'], ['gomel', 'gomel', 'central , gomel', '11800', '3'], ['dnepr - transmash', 'mogilev', 'spartak , mogilev', '11200', '4'], ['shakhtyor', 'soligorsk', 'stroitel', '5000', '5'], ['dinamo minsk', 'minsk', 'dinamo , minsk', '41040', '6'], ['dinamo brest', 'brest', 'dinamo , brest', '10080', '7'], ['belshina', 'bobruisk', 'spartak , bobruisk', '3550', '8'], ['neman - belcard', 'grodno', 'neman', '6300', '9'], ['torpedo - maz', 'minsk', 'torpedo , minsk', '5200', '10'], ['lokomotiv - 96', 'vitebsk', 'central , vitebsk', '8300', '11'], ['naftan - devon', 'novopolotsk', 'atlant', '6500', '12'], ['lida', 'lida', 'city stadium , lida', '4000', '13'], ['torpedo - kadino', 'mogilev', 'torpedo , mogilev', '3500', '14'], ['kommunalnik', 'slonim', 'yunost , slonim', '3000', 'first league , 1'], ['vedrich - 97', 'rechytsa', 'central , rechytsa', '3550', 'first league , 2']] |
naia independent football schools | https://en.wikipedia.org/wiki/NAIA_independent_football_schools | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15617076-1.html.csv | comparative | webber international university was founded several decades earlier than ave maria university . | {'row_1': '11', 'row_2': '1', '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', 'institution', 'webber international university'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose institution record fuzzily matches to webber international university .', 'tostr': 'filter_eq { all_rows ; institution ; webber international university }'}, 'founded'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; institution ; webber international university } ; founded }', 'tointer': 'select the rows whose institution record fuzzily matches to webber international university . take the founded record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'institution', 'ave maria university'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose institution record fuzzily matches to ave maria university .', 'tostr': 'filter_eq { all_rows ; institution ; ave maria university }'}, 'founded'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; institution ; ave maria university } ; founded }', 'tointer': 'select the rows whose institution record fuzzily matches to ave maria university . take the founded record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; institution ; webber international university } ; founded } ; hop { filter_eq { all_rows ; institution ; ave maria university } ; founded } } = true', 'tointer': 'select the rows whose institution record fuzzily matches to webber international university . take the founded record of this row . select the rows whose institution record fuzzily matches to ave maria university . take the founded record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; institution ; webber international university } ; founded } ; hop { filter_eq { all_rows ; institution ; ave maria university } ; founded } } = true | select the rows whose institution record fuzzily matches to webber international university . take the founded record of this row . select the rows whose institution record fuzzily matches to ave maria university . take the founded 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, 'institution_7': 7, 'webber international university_8': 8, 'founded_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'institution_11': 11, 'ave maria university_12': 12, 'founded_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', 'institution_7': 'institution', 'webber international university_8': 'webber international university', 'founded_9': 'founded', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'institution_11': 'institution', 'ave maria university_12': 'ave maria university', 'founded_13': 'founded'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'institution_7': [0], 'webber international university_8': [0], 'founded_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'institution_11': [1], 'ave maria university_12': [1], 'founded_13': [3]} | ['institution', 'location', 'founded', 'type', 'enrollment', 'team', 'primary conference'] | [['ave maria university', 'ave maria , florida', '1998', 'private', '1200', 'gyrenes', 'the sun'], ['dakota state university', 'madison , south dakota', '1881', 'public', '3102', 'trojans', 'none'], ['edward waters college', 'jacksonville , florida', '1866', 'private', '800', 'tigers', 'gulf coast ( gcac )'], ['haskell indian nations university', 'lawrence , kansas', '1884', 'tribal', '1000', 'fighting indians', 'mcac'], ['jamestown college', 'jamestown , north dakota', '1883', 'private', '967', 'jimmies', 'none'], ['lindenwood universitybelleville', 'belleville , illinois', '2003', 'private', '2600', 'lynx', 'none'], ['mayville state university', 'mayville , north dakota', '1889', 'public', '825', 'comets', 'none'], ['menlo college', 'atherton , california', '1927', 'private', '650', 'oaks', 'calpac'], ['point university', 'west point , georgia', '1937', 'private', '1035', 'skyhawks', 'aac'], ['valley city state university', 'valley city , north dakota', '1890', 'public', '1340', 'vikings', 'none'], ['webber international university', 'babson park , florida', '1927', 'private', '616', 'warriors', 'the sun']] |
1998 icc knockout trophy | https://en.wikipedia.org/wiki/1998_ICC_KnockOut_Trophy | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11950720-1.html.csv | unique | michael bevan was the only player with a left arm slow chinaman bowling style . | {'scope': 'all', 'row': '3', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': 'left arm slow chinaman', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'bowling style', 'left arm slow chinaman'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose bowling style record fuzzily matches to left arm slow chinaman .', 'tostr': 'filter_eq { all_rows ; bowling style ; left arm slow chinaman }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; bowling style ; left arm slow chinaman } }', 'tointer': 'select the rows whose bowling style record fuzzily matches to left arm slow chinaman . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'bowling style', 'left arm slow chinaman'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose bowling style record fuzzily matches to left arm slow chinaman .', 'tostr': 'filter_eq { all_rows ; bowling style ; left arm slow chinaman }'}, 'player'], 'result': 'michael bevan', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; bowling style ; left arm slow chinaman } ; player }'}, 'michael bevan'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; bowling style ; left arm slow chinaman } ; player } ; michael bevan }', 'tointer': 'the player record of this unqiue row is michael bevan .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; bowling style ; left arm slow chinaman } } ; eq { hop { filter_eq { all_rows ; bowling style ; left arm slow chinaman } ; player } ; michael bevan } } = true', 'tointer': 'select the rows whose bowling style record fuzzily matches to left arm slow chinaman . there is only one such row in the table . the player record of this unqiue row is michael bevan .'} | and { only { filter_eq { all_rows ; bowling style ; left arm slow chinaman } } ; eq { hop { filter_eq { all_rows ; bowling style ; left arm slow chinaman } ; player } ; michael bevan } } = true | select the rows whose bowling style record fuzzily matches to left arm slow chinaman . there is only one such row in the table . the player record of this unqiue row is michael bevan . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'bowling style_7': 7, 'left arm slow chinaman_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'michael bevan_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'bowling style_7': 'bowling style', 'left arm slow chinaman_8': 'left arm slow chinaman', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'michael bevan_10': 'michael bevan'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'bowling style_7': [0], 'left arm slow chinaman_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'michael bevan_10': [3]} | ['player', 'date of birth', 'batting style', 'bowling style', 'first class team'] | [['steve waugh ( captain )', '2 june 1965', 'right hand bat', 'right arm medium', 'new south wales'], ['mark waugh ( vice - captain )', '2 june 1965', 'right hand bat', 'right arm medium right arm off break', 'new south wales'], ['michael bevan', '8 may 1970', 'left hand bat', 'left arm slow chinaman', 'new south wales'], ['damien fleming', '24 april 1970', 'right hand bat', 'right arm fast - medium', 'victoria'], ['adam gilchrist ( wicket - keeper )', '14 november 1971', 'left hand bat', 'wicket - keeper', 'western australia'], ['brendon julian', '10 august 1970', 'right hand bat', 'left arm fast - medium', 'western australia'], ['michael kasprowicz', '10 february 1972', 'right hand bat', 'right arm fast - medium', 'queensland'], ['darren lehmann', '5 february 1970', 'left hand bat', 'left arm orthodox spin', 'south australia'], ['damien martyn', '21 october 1971', 'right hand bat', 'right arm medium', 'western australia'], ['glenn mcgrath', '9 february 1970', 'right hand bat', 'right arm fast - medium', 'new south wales'], ['ricky ponting', '19 december 1974', 'right hand bat', 'right arm medium', 'tasmania'], ['gavin robertson', '28 may 1966', 'right hand bat', 'right arm off break', 'new south wales'], ['andrew symonds', '9 june 1975', 'right hand bat', 'right arm medium right arm off break', 'queensland'], ['brad young', '23 february 1973', 'right hand bat', 'left arm orthodox spin', 'south australia']] |
hughes hall college boat club | https://en.wikipedia.org/wiki/Hughes_Hall_College_Boat_Club | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18880596-2.html.csv | comparative | the hughes hall college boat club finished two positions better in 2009 than in 2008 . | {'row_1': '2', 'row_2': '1', 'col': '2', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'yes', 'diff_result': None} | {'func': 'and', 'args': [{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '2009'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record fuzzily matches to 2009 .', 'tostr': 'filter_eq { all_rows ; year ; 2009 }'}, 'finish position'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; year ; 2009 } ; finish position }', 'tointer': 'select the rows whose year record fuzzily matches to 2009 . take the finish position record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '2008'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year record fuzzily matches to 2008 .', 'tostr': 'filter_eq { all_rows ; year ; 2008 }'}, 'finish position'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; year ; 2008 } ; finish position }', 'tointer': 'select the rows whose year record fuzzily matches to 2008 . take the finish position record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; year ; 2009 } ; finish position } ; hop { filter_eq { all_rows ; year ; 2008 } ; finish position } }', 'tointer': 'select the rows whose year record fuzzily matches to 2009 . take the finish position record of this row . select the rows whose year record fuzzily matches to 2008 . take the finish position record of this row . the first record is less than the second record .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '2009'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record fuzzily matches to 2009 .', 'tostr': 'filter_eq { all_rows ; year ; 2009 }'}, 'finish position'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; year ; 2009 } ; finish position }', 'tointer': 'select the rows whose year record fuzzily matches to 2009 . take the finish position record of this row .'}, '31st'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; year ; 2009 } ; finish position } ; 31st }', 'tointer': 'the finish position record of the first row is 31st .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '2008'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year record fuzzily matches to 2008 .', 'tostr': 'filter_eq { all_rows ; year ; 2008 }'}, 'finish position'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; year ; 2008 } ; finish position }', 'tointer': 'select the rows whose year record fuzzily matches to 2008 . take the finish position record of this row .'}, '33rd'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; year ; 2008 } ; finish position } ; 33rd }', 'tointer': 'the finish position record of the second row is 33rd .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; year ; 2009 } ; finish position } ; 31st } ; eq { hop { filter_eq { all_rows ; year ; 2008 } ; finish position } ; 33rd } }', 'tointer': 'the finish position record of the first row is 31st . the finish position record of the second row is 33rd .'}], 'result': True, 'ind': 8, 'tostr': 'and { less { hop { filter_eq { all_rows ; year ; 2009 } ; finish position } ; hop { filter_eq { all_rows ; year ; 2008 } ; finish position } } ; and { eq { hop { filter_eq { all_rows ; year ; 2009 } ; finish position } ; 31st } ; eq { hop { filter_eq { all_rows ; year ; 2008 } ; finish position } ; 33rd } } } = true', 'tointer': 'select the rows whose year record fuzzily matches to 2009 . take the finish position record of this row . select the rows whose year record fuzzily matches to 2008 . take the finish position record of this row . the first record is less than the second record . the finish position record of the first row is 31st . the finish position record of the second row is 33rd .'} | and { less { hop { filter_eq { all_rows ; year ; 2009 } ; finish position } ; hop { filter_eq { all_rows ; year ; 2008 } ; finish position } } ; and { eq { hop { filter_eq { all_rows ; year ; 2009 } ; finish position } ; 31st } ; eq { hop { filter_eq { all_rows ; year ; 2008 } ; finish position } ; 33rd } } } = true | select the rows whose year record fuzzily matches to 2009 . take the finish position record of this row . select the rows whose year record fuzzily matches to 2008 . take the finish position record of this row . the first record is less than the second record . the finish position record of the first row is 31st . the finish position record of the second row is 33rd . | 13 | 9 | {'and_8': 8, 'result_9': 9, 'less_4': 4, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'year_11': 11, '2009_12': 12, 'finish position_13': 13, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'year_15': 15, '2008_16': 16, 'finish position_17': 17, 'and_7': 7, 'str_eq_5': 5, '31st_18': 18, 'str_eq_6': 6, '33rd_19': 19} | {'and_8': 'and', 'result_9': 'true', 'less_4': 'less', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'year_11': 'year', '2009_12': '2009', 'finish position_13': 'finish position', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'year_15': 'year', '2008_16': '2008', 'finish position_17': 'finish position', 'and_7': 'and', 'str_eq_5': 'str_eq', '31st_18': '31st', 'str_eq_6': 'str_eq', '33rd_19': '33rd'} | {'and_8': [9], 'result_9': [], 'less_4': [8], 'str_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'year_11': [0], '2009_12': [0], 'finish position_13': [2], 'str_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'year_15': [1], '2008_16': [1], 'finish position_17': [3], 'and_7': [8], 'str_eq_5': [7], '31st_18': [5], 'str_eq_6': [7], '33rd_19': [6]} | ['year', 'finish position', '1st day', '2nd day', '3rd day', '4th day'] | [['2008', '33rd', 'bumped corpus christi / newnham', 'rowed - over', 'rowed - over', 'bumped wolfson'], ['2009', '31st', "bumped st edmund 's", 'rowed - over', 'bumped darwin', 'rowed - over'], ['2010', '31st', 'bumped by corpus christi', 'rowed - over', 'bumped caius', 'rowed - over'], ['2011', '27th', 'bumped anglia ruskin', 'bumped pembroke', 'bumped homerton', 'bumped robinson'], ['2012', '27th', 'bumped jesus', 'rowed - over', 'bumped by robinson', 'bumped by homerton']] |
vivian girls | https://en.wikipedia.org/wiki/Vivian_Girls | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18710512-3.html.csv | aggregation | the singles of the group vivian girls sold an average of 2250 copies per single . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '2250', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'other details'], 'result': '2250', 'ind': 0, 'tostr': 'avg { all_rows ; other details }'}, '2250'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; other details } ; 2250 } = true', 'tointer': 'the average of the other details record of all rows is 2250 .'} | round_eq { avg { all_rows ; other details } ; 2250 } = true | the average of the other details record of all rows is 2250 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'other details_4': 4, '2250_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'other details_4': 'other details', '2250_5': '2250'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'other details_4': [0], '2250_5': [1]} | ['date', 'single', 'backed with', 'record label', 'format', 'other details'] | [['2008', 'wild eyes', 'my baby wants me dead', 'plays with dolls / wild world', '7 single', '4000 copies'], ['2008', 'tell the world', 'i believe in nothing & damaged', 'woodsist', '7 single', '3000 copies'], ['2008', "i ca n't stay", 'blind spot', 'in the red', '7 single', '2000 copies'], ['2008', 'surfin away & second date', "girl do n't tell me ( wilson )", 'wild world', '7 single', '1000 copies'], ['2009', 'moped girls', 'death', 'for us', '7 single', '1500 copies'], ['2010', 'my love will follow me', "he 's gone ( the chantels cover )", 'wild world', '7 single', '2000 copies']] |
list of leverage episodes | https://en.wikipedia.org/wiki/List_of_Leverage_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-20704243-3.html.csv | count | 2 episodes of the series leverage both had 3.69 us viewers in millions . | {'scope': 'all', 'criterion': 'equal', 'value': '3.69', 'result': '2', 'col': '7', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'us viewers ( in millions )', '3.69'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose us viewers ( in millions ) record is equal to 3.69 .', 'tostr': 'filter_eq { all_rows ; us viewers ( in millions ) ; 3.69 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; us viewers ( in millions ) ; 3.69 } }', 'tointer': 'select the rows whose us viewers ( in millions ) record is equal to 3.69 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; us viewers ( in millions ) ; 3.69 } } ; 2 } = true', 'tointer': 'select the rows whose us viewers ( in millions ) record is equal to 3.69 . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; us viewers ( in millions ) ; 3.69 } } ; 2 } = true | select the rows whose us viewers ( in millions ) record is equal to 3.69 . 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, 'us viewers (in millions)_5': 5, '3.69_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'us viewers (in millions)_5': 'us viewers ( in millions )', '3.69_6': '3.69', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'us viewers (in millions)_5': [0], '3.69_6': [0], '2_7': [2]} | ['series', 'season', 'title', 'directed by', 'written by', 'original air date', 'us viewers ( in millions )'] | [['14', '1', 'the beantown bailout job', 'dean devlin', 'john rogers', 'july 15 , 2009', '3.89'], ['15', '2', 'the tap - out job', 'marc roskin', 'albert kim', 'july 22 , 2009', '3.05'], ['16', '3', 'the order 23 job', 'rod hardy', 'chris downey', 'july 29 , 2009', '3.68'], ['17', '4', 'the fairy godparents job', 'jonathan frakes', 'amy berg', 'august 5 , 2009', '3.69'], ['18', '5', 'the three days of the hunter job', 'marc roskin', 'melissa glenn & jessica rieder', 'august 12 , 2009', '3.16'], ['19', '6', 'the top hat job', "peter o'fallon", 'm scott veach & christine boylan', 'august 19 , 2009', '3.43'], ['20', '7', 'the two live crew job', 'dean devlin', 'amy berg & john rogers', 'august 26 , 2009', '3.73'], ['21', '8', 'the ice man job', 'jeremiah chechik', 'christine boylan', 'september 2 , 2009', '3.58'], ['22', '9', 'the lost heir job', 'peter winther', 'chris downey', 'september 9 , 2009', '3.37'], ['23', '10', 'the runway job', 'mark roskin', 'albert kim', 'january 13 , 2010', '3.69'], ['24', '11', 'the bottle job', 'jonathan frakes', 'christine boylan', 'january 20 , 2010', '3.00'], ['25', '12', 'the zanzibar marketplace job', 'jeremiah chechik', 'melissa glenn & jessica rieder', 'january 27 , 2010', '3.02'], ['26', '13', 'the future job', 'mark roskin', 'amy berg & chris downey', 'february 3 , 2010', '2.91'], ['27', '14', 'the three strikes job', 'dean devlin', 'john rogers', 'february 10 , 2010', '2.8']] |
2007 - 08 san antonio spurs season | https://en.wikipedia.org/wiki/2007%E2%80%9308_San_Antonio_Spurs_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11963601-6.html.csv | count | in the 2007 - 08 san antonio spurs season , among the games where spurs were visitors , 3 of them had attendance below 18,000 . | {'scope': 'subset', 'criterion': 'less_than', 'value': '18000', 'result': '3', 'col': '6', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'spurs'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_less', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'visitor', 'spurs'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; visitor ; spurs }', 'tointer': 'select the rows whose visitor record fuzzily matches to spurs .'}, 'attendance', '18000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose visitor record fuzzily matches to spurs . among these rows , select the rows whose attendance record is less than 18000 .', 'tostr': 'filter_less { filter_eq { all_rows ; visitor ; spurs } ; attendance ; 18000 }'}], 'result': '3', 'ind': 2, 'tostr': 'count { filter_less { filter_eq { all_rows ; visitor ; spurs } ; attendance ; 18000 } }', 'tointer': 'select the rows whose visitor record fuzzily matches to spurs . among these rows , select the rows whose attendance record is less than 18000 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_less { filter_eq { all_rows ; visitor ; spurs } ; attendance ; 18000 } } ; 3 } = true', 'tointer': 'select the rows whose visitor record fuzzily matches to spurs . among these rows , select the rows whose attendance record is less than 18000 . the number of such rows is 3 .'} | eq { count { filter_less { filter_eq { all_rows ; visitor ; spurs } ; attendance ; 18000 } } ; 3 } = true | select the rows whose visitor record fuzzily matches to spurs . among these rows , select the rows whose attendance record is less than 18000 . the number of such rows is 3 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_less_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'visitor_6': 6, 'spurs_7': 7, 'attendance_8': 8, '18000_9': 9, '3_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_less_1': 'filter_less', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'visitor_6': 'visitor', 'spurs_7': 'spurs', 'attendance_8': 'attendance', '18000_9': '18000', '3_10': '3'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_less_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'visitor_6': [0], 'spurs_7': [0], 'attendance_8': [1], '18000_9': [1], '3_10': [3]} | ['date', 'visitor', 'score', 'home', 'leading scorer', 'attendance', 'record'] | [['january 3 , 2008', 'spurs', '77 - 80', 'nuggets', 'two - way tie ( 20 )', '19155', '21 - 9'], ['january 4 , 2008', 'knicks', '93 - 97', 'spurs', 'bruce bowen ( 15 )', '18797', '22 - 9'], ['january 6 , 2008', 'spurs', '88 - 82', 'clippers', 'tony parker ( 26 )', '16623', '23 - 9'], ['january 7 , 2008', 'spurs', '121 - 130', 'warriors', 'tim duncan ( 32 )', '19107', '23 - 10'], ['january 10 , 2008', 'pistons', '90 - 80', 'spurs', 'tim duncan ( 24 )', '18797', '23 - 11'], ['january 12 , 2008', 'timberwolves', '88 - 105', 'spurs', 'manu ginóbili ( 22 )', '18797', '24 - 11'], ['january 14 , 2008', 'sixers', '82 - 89', 'spurs', 'manu ginóbili ( 20 )', '17609', '25 - 11'], ['january 17 , 2008', 'cavaliers', '90 - 88', 'spurs', 'manu ginóbili ( 31 )', '18482', '25 - 12'], ['january 19 , 2008', 'spurs', '81 - 83', 'rockets', 'tim duncan ( 24 )', '18353', '25 - 13'], ['january 21 , 2008', 'spurs', '95 - 86', 'bobcats', 'tim duncan ( 19 )', '17124', '26 - 13'], ['january 23 , 2008', 'lakers', '91 - 103', 'spurs', 'tim duncan ( 28 )', '18797', '27 - 13'], ['january 24 , 2008', 'spurs', '90 - 89', 'heat', 'tim duncan ( 30 )', '19600', '28 - 13'], ['january 26 , 2008', 'hornets', '102 - 78', 'spurs', 'two - way tie ( 17 )', '18797', '28 - 14'], ['january 28 , 2008', 'spurs', '91 - 99', 'jazz', 'manu ginóbili ( 29 )', '19911', '28 - 15'], ['january 29 , 2008', 'spurs', '85 - 88', 'supersonics', 'manu ginóbili ( 29 )', '13295', '28 - 16'], ['january 31 , 2008', 'spurs', '84 - 81', 'suns', 'manu ginóbili ( 19 )', '18422', '29 - 16']] |
water resources management in chile | https://en.wikipedia.org/wiki/Water_resources_management_in_Chile | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22854436-1.html.csv | ordinal | in the water resources management in chile , i - tarapacá is the administrative region with the highest average annual runoff ( mm ) among those with average annual rainfall ( mm ) less than 100 . | {'scope': 'subset', 'row': '1', 'col': '6', 'order': '1', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'subset': {'col': '5', 'criterion': 'less_than', 'value': '100'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'average annual rainfall ( mm )', '100'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; average annual rainfall ( mm ) ; 100 }', 'tointer': 'select the rows whose average annual rainfall ( mm ) record is less than 100 .'}, 'average annual runoff ( mm )', '1'], 'result': None, 'ind': 1, 'tostr': 'nth_argmax { filter_less { all_rows ; average annual rainfall ( mm ) ; 100 } ; average annual runoff ( mm ) ; 1 }'}, 'administrative region'], 'result': 'i - tarapacá', 'ind': 2, 'tostr': 'hop { nth_argmax { filter_less { all_rows ; average annual rainfall ( mm ) ; 100 } ; average annual runoff ( mm ) ; 1 } ; administrative region }'}, 'i - tarapacá'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmax { filter_less { all_rows ; average annual rainfall ( mm ) ; 100 } ; average annual runoff ( mm ) ; 1 } ; administrative region } ; i - tarapacá } = true', 'tointer': 'select the rows whose average annual rainfall ( mm ) record is less than 100 . select the row whose average annual runoff ( mm ) record of these rows is 1st maximum . the administrative region record of this row is i - tarapacá .'} | eq { hop { nth_argmax { filter_less { all_rows ; average annual rainfall ( mm ) ; 100 } ; average annual runoff ( mm ) ; 1 } ; administrative region } ; i - tarapacá } = true | select the rows whose average annual rainfall ( mm ) record is less than 100 . select the row whose average annual runoff ( mm ) record of these rows is 1st maximum . the administrative region record of this row is i - tarapacá . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmax_1': 1, 'filter_less_0': 0, 'all_rows_5': 5, 'average annual rainfall (mm)_6': 6, '100_7': 7, 'average annual runoff (mm)_8': 8, '1_9': 9, 'administrative region_10': 10, 'i - tarapacá_11': 11} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmax_1': 'nth_argmax', 'filter_less_0': 'filter_less', 'all_rows_5': 'all_rows', 'average annual rainfall (mm)_6': 'average annual rainfall ( mm )', '100_7': '100', 'average annual runoff (mm)_8': 'average annual runoff ( mm )', '1_9': '1', 'administrative region_10': 'administrative region', 'i - tarapacá_11': 'i - tarapacá'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmax_1': [2], 'filter_less_0': [1], 'all_rows_5': [0], 'average annual rainfall (mm)_6': [0], '100_7': [0], 'average annual runoff (mm)_8': [1], '1_9': [1], 'administrative region_10': [2], 'i - tarapacá_11': [3]} | ['administrative region', 'population ( 2002 census data )', 'surface km 2', 'main rivers', 'average annual rainfall ( mm )', 'average annual runoff ( mm )', 'per capita average annual renewable water resources m 3'] | [['i - tarapacá', '428594', '58698', 'azapa river , vítor river and camarones river', '93.6', '7.1', '972'], ['ii - antofagasta', '493984', '126444', 'loa river', '44.5', '0.2', '51'], ['iii - atacama', '254336', '75573', 'salado river', '82.4', '0.7', '208'], ['iv - coquimbo', '603210', '40656', 'elqui river , choapa river and limarí river', '222', '18', '1213'], ['v - valparaíso', '1539852', '16396', 'petorca river , la ligua river and aconcagua river', '434', '84', '894'], ['metro region ( mr ) - santiago metropolitan', '7003122', '15349', 'maipo river', '650', '200', '438'], ['vii - maule', '908097', '30325', 'mataquito river and maule river', '1377', '784', '26181'], ['viii - biobío', '1861562', '36929', 'itata river , biobío river and laja river', '1766', '1173', '23270']] |
1975 vfl season | https://en.wikipedia.org/wiki/1975_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10883333-3.html.csv | ordinal | princes park venue recorded the 2nd highest crowd participation during the 1975 vfl season . | {'row': '3', 'col': '6', 'order': '2', 'col_other': '5', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'crowd', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; crowd ; 2 }'}, 'venue'], 'result': 'princes park', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; crowd ; 2 } ; venue }'}, 'princes park'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; princes park } = true', 'tointer': 'select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is princes park .'} | eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; princes park } = true | select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is princes park . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'crowd_5': 5, '2_6': 6, 'venue_7': 7, 'princes park_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'crowd_5': 'crowd', '2_6': '2', 'venue_7': 'venue', 'princes park_8': 'princes park'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'crowd_5': [0], '2_6': [0], 'venue_7': [1], 'princes park_8': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['st kilda', '11.21 ( 87 )', 'south melbourne', '9.17 ( 71 )', 'moorabbin oval', '15736', '19 april 1975'], ['essendon', '16.21 ( 117 )', 'melbourne', '15.10 ( 100 )', 'windy hill', '22824', '19 april 1975'], ['carlton', '14.18 ( 102 )', 'north melbourne', '9.12 ( 66 )', 'princes park', '23824', '19 april 1975'], ['geelong', '13.12 ( 90 )', 'footscray', '14.14 ( 98 )', 'kardinia park', '17158', '19 april 1975'], ['fitzroy', '12.13 ( 85 )', 'collingwood', '12.15 ( 87 )', 'junction oval', '17626', '19 april 1975'], ['hawthorn', '17.14 ( 116 )', 'richmond', '12.15 ( 87 )', 'vfl park', '39496', '19 april 1975']] |
2008 armenian cup | https://en.wikipedia.org/wiki/2008_Armenian_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17372848-1.html.csv | unique | the 1st leg match between mika and ararat-2 was the only match to end in a 7-0 score in the 2008 armenian cup . | {'scope': 'all', 'row': '5', 'col': '4', 'col_other': '1,3', 'criterion': 'equal', 'value': '7 - 0', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '1st leg', '7 - 0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 1st leg record fuzzily matches to 7 - 0 .', 'tostr': 'filter_eq { all_rows ; 1st leg ; 7 - 0 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; 1st leg ; 7 - 0 } }', 'tointer': 'select the rows whose 1st leg record fuzzily matches to 7 - 0 . there is only one such row in the table .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '1st leg', '7 - 0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 1st leg record fuzzily matches to 7 - 0 .', 'tostr': 'filter_eq { all_rows ; 1st leg ; 7 - 0 }'}, 'team 1'], 'result': 'mika', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; 1st leg ; 7 - 0 } ; team 1 }'}, 'mika'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; 1st leg ; 7 - 0 } ; team 1 } ; mika }', 'tointer': 'the team 1 record of this unqiue row is mika .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '1st leg', '7 - 0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 1st leg record fuzzily matches to 7 - 0 .', 'tostr': 'filter_eq { all_rows ; 1st leg ; 7 - 0 }'}, 'team 2'], 'result': 'ararat - 2', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; 1st leg ; 7 - 0 } ; team 2 }'}, 'ararat - 2'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; 1st leg ; 7 - 0 } ; team 2 } ; ararat - 2 }', 'tointer': 'the team 2 record of this unqiue row is ararat - 2 .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_eq { all_rows ; 1st leg ; 7 - 0 } ; team 1 } ; mika } ; eq { hop { filter_eq { all_rows ; 1st leg ; 7 - 0 } ; team 2 } ; ararat - 2 } }', 'tointer': 'the team 1 record of this unqiue row is mika . the team 2 record of this unqiue row is ararat - 2 .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_eq { all_rows ; 1st leg ; 7 - 0 } } ; and { eq { hop { filter_eq { all_rows ; 1st leg ; 7 - 0 } ; team 1 } ; mika } ; eq { hop { filter_eq { all_rows ; 1st leg ; 7 - 0 } ; team 2 } ; ararat - 2 } } } = true', 'tointer': 'select the rows whose 1st leg record fuzzily matches to 7 - 0 . there is only one such row in the table . the team 1 record of this unqiue row is mika . the team 2 record of this unqiue row is ararat - 2 .'} | and { only { filter_eq { all_rows ; 1st leg ; 7 - 0 } } ; and { eq { hop { filter_eq { all_rows ; 1st leg ; 7 - 0 } ; team 1 } ; mika } ; eq { hop { filter_eq { all_rows ; 1st leg ; 7 - 0 } ; team 2 } ; ararat - 2 } } } = true | select the rows whose 1st leg record fuzzily matches to 7 - 0 . there is only one such row in the table . the team 1 record of this unqiue row is mika . the team 2 record of this unqiue row is ararat - 2 . | 10 | 8 | {'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_9': 9, '1st leg_10': 10, '7 - 0_11': 11, 'and_6': 6, 'str_eq_3': 3, 'str_hop_2': 2, 'team 1_12': 12, 'mika_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'team 2_14': 14, 'ararat - 2_15': 15} | {'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_9': 'all_rows', '1st leg_10': '1st leg', '7 - 0_11': '7 - 0', 'and_6': 'and', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'team 1_12': 'team 1', 'mika_13': 'mika', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'team 2_14': 'team 2', 'ararat - 2_15': 'ararat - 2'} | {'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_str_eq_0': [1, 2, 4], 'all_rows_9': [0], '1st leg_10': [0], '7 - 0_11': [0], 'and_6': [7], 'str_eq_3': [6], 'str_hop_2': [3], 'team 1_12': [2], 'mika_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'team 2_14': [4], 'ararat - 2_15': [5]} | ['team 1', 'agg', 'team 2', '1st leg', '2nd leg'] | [['banants - 2', '2 - 6', 'ulisses', '1 - 4', '1 - 2'], ['pyunik', '15 - 2', 'patani', '11 - 2', '4 - 0'], ['gandzasar', '8 - 0', 'pyunik - 2', '3 - 0', '5 - 0'], ['kilikia', '5 - 2', 'mika - 2', '2 - 0', '3 - 2'], ['mika', '11 - 0', 'ararat - 2', '7 - 0', '4 - 0'], ['shengavit', '2 - 3', 'shirak', '1 - 3', '1 - 0']] |
1963 - 64 segunda división | https://en.wikipedia.org/wiki/1963%E2%80%9364_Segunda_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17740819-4.html.csv | majority | all clubs which participated in the 1963 - 64 segunda división season games each played 30 matches . | {'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': '30', 'subset': None} | {'func': 'all_eq', 'args': ['all_rows', 'played', '30'], 'result': True, 'ind': 0, 'tointer': 'for the played records of all rows , all of them are equal to 30 .', 'tostr': 'all_eq { all_rows ; played ; 30 } = true'} | all_eq { all_rows ; played ; 30 } = true | for the played records of all rows , all of them are equal to 30 . | 1 | 1 | {'all_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'played_3': 3, '30_4': 4} | {'all_eq_0': 'all_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'played_3': 'played', '30_4': '30'} | {'all_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'played_3': [0], '30_4': [0]} | ['position', 'club', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference'] | [['1', 'ud las palmas', '30', '40', '17', '6', '7', '45', '25', '+ 20'], ['2', 'hércules cf', '30', '38', '15', '8', '7', '48', '38', '+ 10'], ['3', 'rcd mallorca', '30', '37', '16', '5', '9', '52', '32', '+ 20'], ['4', 'cd mestalla', '30', '33', '13', '7', '10', '60', '38', '+ 22'], ['5', 'cd tenerife', '30', '32', '14', '4', '12', '30', '40', '- 10'], ['6', 'granada cf', '30', '32', '12', '8', '10', '41', '32', '+ 9'], ['7', 'cádiz cf', '30', '30', '13', '4', '13', '45', '41', '+ 4'], ['8', 'algeciras cf', '30', '30', '13', '4', '13', '40', '53', '- 13'], ['9', 'cd málaga', '30', '30', '12', '6', '12', '38', '33', '+ 5'], ['10', 'onteniente cf', '30', '29', '10', '9', '11', '33', '29', '+ 4'], ['11', 'recreativo de huelva', '30', '29', '10', '9', '11', '41', '34', '+ 7'], ['12', 'melilla cf', '30', '28', '10', '8', '12', '34', '38', '- 4'], ['13', 'cd abarán', '30', '26', '10', '6', '14', '38', '49', '- 11'], ['14', 'atlético ceuta', '30', '26', '10', '6', '14', '29', '49', '- 20'], ['15', 'cd san fernando', '30', '21', '9', '3', '18', '24', '45', '- 21'], ['16', 'cd eldense', '30', '19', '7', '5', '18', '33', '55', '- 22']] |
b.g. discography | https://en.wikipedia.org/wiki/B.G._discography | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18519524-3.html.csv | count | b.g. discography had two us hot 100 songs from 1999 to 2010 . | {'scope': 'all', 'criterion': 'not_equal', 'value': '-', 'result': '2', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_not_eq', 'args': ['all_rows', 'us hot 100', '-'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose us hot 100 record is not equal to - .', 'tostr': 'filter_not_eq { all_rows ; us hot 100 ; - }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_not_eq { all_rows ; us hot 100 ; - } }', 'tointer': 'select the rows whose us hot 100 record is not equal to - . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_not_eq { all_rows ; us hot 100 ; - } } ; 2 } = true', 'tointer': 'select the rows whose us hot 100 record is not equal to - . the number of such rows is 2 .'} | eq { count { filter_not_eq { all_rows ; us hot 100 ; - } } ; 2 } = true | select the rows whose us hot 100 record is not equal to - . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_not_eq_0': 0, 'all_rows_4': 4, 'us hot 100_5': 5, '-_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_not_eq_0': 'filter_not_eq', 'all_rows_4': 'all_rows', 'us hot 100_5': 'us hot 100', '-_6': '-', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_not_eq_0': [1], 'all_rows_4': [0], 'us hot 100_5': [0], '-_6': [0], '2_7': [2]} | ['year', 'us hot 100', 'us r & b', 'us rap', 'album'] | [['1999', '36', '13', '10', 'chopper city in the ghetto'], ['1999', '-', '106', '-', 'chopper city in the ghetto'], ['2000', '-', '86', '-', 'checkmate'], ['2003', '-', '74', '-', "livin ' legend"], ['2003', '-', '-', '-', "livin ' legend"], ['2004', '-', '105', '-', 'life after cash money'], ['2005', '-', '65', '-', 'the heart of tha streetz , vol 1'], ['2006', '113', '52', '-', 'the heart of tha streetz , vol 2 ( i am what i am )'], ['2007', '-', '-', '-', 'we got this'], ['2008', '-', '-', '-', 'life in the concrete jungle'], ['2008', '-', '-', '-', 'too hood 2 be hollywood'], ['2009', '-', '-', '-', 'too hood 2 be hollywood'], ['2009', '-', '70', '-', 'too hood 2 be hollywood'], ['2010', '-', '-', '-', 'too hood 2 be hollywood']] |
1950 vfl season | https://en.wikipedia.org/wiki/1950_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10701045-15.html.csv | count | there were 6 game venues used during the 1950 vfl season . | {'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '6', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'venue'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record is arbitrary .', 'tostr': 'filter_all { all_rows ; venue }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; venue } }', 'tointer': 'select the rows whose venue record is arbitrary . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; venue } } ; 6 } = true', 'tointer': 'select the rows whose venue record is arbitrary . the number of such rows is 6 .'} | eq { count { filter_all { all_rows ; venue } } ; 6 } = true | select the rows whose venue 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, 'venue_5': 5, '6_6': 6} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'venue_5': 'venue', '6_6': '6'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'venue_5': [0], '6_6': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['north melbourne', '15.14 ( 104 )', 'st kilda', '7.5 ( 47 )', 'arden street oval', '9000', '5 august 1950'], ['geelong', '13.14 ( 92 )', 'melbourne', '9.12 ( 66 )', 'kardinia park', '15500', '5 august 1950'], ['collingwood', '16.21 ( 117 )', 'hawthorn', '2.8 ( 20 )', 'victoria park', '9000', '5 august 1950'], ['south melbourne', '11.8 ( 74 )', 'fitzroy', '11.11 ( 77 )', 'lake oval', '9000', '5 august 1950'], ['footscray', '10.9 ( 69 )', 'essendon', '10.11 ( 71 )', 'western oval', '13000', '5 august 1950'], ['richmond', '14.20 ( 104 )', 'carlton', '9.16 ( 70 )', 'punt road oval', '18000', '5 august 1950']] |
united states house of representatives elections , 2006 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_2006 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1805191-48.html.csv | majority | all incumbents of the 2006 house of representatives elections were re - elected . | {'scope': 'all', 'col': '5', 'most_or_all': 'all', 'criterion': 'equal', 'value': 're - elected', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'results', 're - elected'], 'result': True, 'ind': 0, 'tointer': 'for the results records of all rows , all of them fuzzily match to re - elected .', 'tostr': 'all_eq { all_rows ; results ; re - elected } = true'} | all_eq { all_rows ; results ; re - elected } = true | for the results records of all rows , all of them fuzzily match to re - elected . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'results_3': 3, 're - elected_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'results_3': 'results', 're - elected_4': 're - elected'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'results_3': [0], 're - elected_4': [0]} | ['district', 'incumbent', 'party', 'first elected', 'results'] | [['washington 1', 'jay inslee', 'democratic', '1998', 're - elected'], ['washington 2', 'rick larsen', 'democratic', '2000', 're - elected'], ['washington 3', 'brian baird', 'democratic', '1998', 're - elected'], ['washington 4', 'doc hastings', 'republican', '1994', 're - elected'], ['washington 5', 'cathy mcmorris', 'republican', '2004', 're - elected'], ['washington 6', 'norm dicks', 'democratic', '1976', 're - elected'], ['washington 7', 'jim mcdermott', 'democratic', '1988', 're - elected'], ['washington 8', 'dave reichert', 'republican', '2004', 're - elected'], ['washington 9', 'adam smith', 'democratic', '1996', 're - elected']] |
just a closer walk with thee ( album ) | https://en.wikipedia.org/wiki/Just_a_Closer_Walk_with_Thee_%28album%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13536392-2.html.csv | ordinal | on the album just a closer walk with thee , the song my lord what a mornin ' is the 3rd shortest . | {'row': '8', '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', 'time', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; time ; 3 }'}, 'title'], 'result': "my lord what a mornin '", 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; time ; 3 } ; title }'}, "my lord what a mornin '"], 'result': True, 'ind': 2, 'tostr': "eq { hop { nth_argmin { all_rows ; time ; 3 } ; title } ; my lord what a mornin ' } = true", 'tointer': "select the row whose time record of all rows is 3rd minimum . the title record of this row is my lord what a mornin ' ."} | eq { hop { nth_argmin { all_rows ; time ; 3 } ; title } ; my lord what a mornin ' } = true | select the row whose time record of all rows is 3rd minimum . the title record of this row is my lord what a mornin ' . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'time_5': 5, '3_6': 6, 'title_7': 7, "my lord what a mornin'_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', 'time_5': 'time', '3_6': '3', 'title_7': 'title', "my lord what a mornin'_8": "my lord what a mornin '"} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'time_5': [0], '3_6': [0], 'title_7': [1], "my lord what a mornin'_8": [2]} | ['track number', 'title', 'songwriter ( s )', 'recording date', 'time'] | [['1', 'swing low , sweet chariot', 'wallis willis ( adapted by malcolm dodds )', 'november 13 , 1959', '3:15'], ['2', 'steal away', '( adapted by malcolm dodds )', 'november 13 , 1959', '3:15'], ['3', 'little david', '( adapted by malcolm dodds )', 'january 28 , 1960', '2:20'], ['4', 'nobody knows', '( adapted by malcolm dodds )', 'november 13 , 1959', '3:10'], ['5', "i could n't hear nobody pray", '( adapted by malcolm dodds )', 'november 16 , 1959', '2:55'], ['6', 'motherless child', 'traditional ( adapted by malcolm dodds )', 'november 13 , 1959', '2:48'], ['7', 'just a closer walk with thee', 'stuart hine ( adapted by malcolm dodds )', 'november 16 , 1959', '3:30'], ['8', "my lord what a mornin '", 'h t burleigh ( adapted by malcolm dodds )', 'january 28 , 1960', '2:30'], ['9', "great getting up mornin '", '( adapted by malcolm dodds )', 'january 28 , 1960', '3:25'], ['10', 'were you there', '( adapted by malcolm dodds )', 'november 16 , 1959', '3:23'], ['11', 'break bread', '( adapted by malcolm dodds )', 'november 16 , 1959', '3:25'], ['12', 'me ! oh lord', '( adapted by malcolm dodds )', 'november 13 , 1959', '2:10']] |
colonial turf cup | https://en.wikipedia.org/wiki/Colonial_Turf_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11237859-1.html.csv | majority | the majority of these races had a distance in miles of 1-3 / 16 . | {'scope': 'all', 'col': '6', 'most_or_all': 'all', 'criterion': 'equal', 'value': '1-3 / 16', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'distance ( miles )', '1-3 / 16'], 'result': True, 'ind': 0, 'tointer': 'for the distance ( miles ) records of all rows , all of them fuzzily match to 1-3 / 16 .', 'tostr': 'all_eq { all_rows ; distance ( miles ) ; 1-3 / 16 } = true'} | all_eq { all_rows ; distance ( miles ) ; 1-3 / 16 } = true | for the distance ( miles ) records of all rows , all of them fuzzily match to 1-3 / 16 . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'distance (miles)_3': 3, '1-3 / 16_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'distance (miles)_3': 'distance ( miles )', '1-3 / 16_4': '1-3 / 16'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'distance (miles)_3': [0], '1-3 / 16_4': [0]} | ['year', 'winner', 'jockey', 'trainer', 'owner', 'distance ( miles )', 'time'] | [['2011', 'rahystrada', 'sheldon russell', 'byron hughes', 'robert courtney', '1 - 3 / 16', '1:54.68'], ['2010', "paddy o'prado", 'kent desormeaux', 'dale romans', 'donegal racing', '1 - 3 / 16', '1:54.20'], ['2009', 'battle of hastings', 'tyler baze', 'jeff mullins', 'michael house', '1 - 3 / 16', '1:57.79'], ['2008', "sailor 's cap", 'alan garcia', 'james j toner', 'team valor international', '1 - 3 / 16', '2:04.42'], ['2007', 'summer doldrums', 'jose lezcano', 'richard a violette , jr', 'klaravich stables', '1 - 3 / 16', '1:55.68'], ['2006', 'showing up', 'cornelio velã ¡ squez', 'barclay tagg', 'lael stables', '1 - 3 / 16', '1:52.98'], ['2005', 'english channel', 'john velazquez', 'todd pletcher', 'james t scatuorchio', '1 - 3 / 16', '1:56:37']] |
weightlifting at the 2007 pan american games | https://en.wikipedia.org/wiki/Weightlifting_at_the_2007_Pan_American_Games | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17703223-5.html.csv | majority | a majority of competitors in the weightlifting competition completed at least a 140 snatch . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'greater_than_eq', 'value': '140', 'subset': None} | {'func': 'most_greater_eq', 'args': ['all_rows', 'snatch', '140'], 'result': True, 'ind': 0, 'tointer': 'for the snatch records of all rows , most of them are greater than or equal to 140 .', 'tostr': 'most_greater_eq { all_rows ; snatch ; 140 } = true'} | most_greater_eq { all_rows ; snatch ; 140 } = true | for the snatch records of all rows , most of them are greater than or equal to 140 . | 1 | 1 | {'most_greater_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'snatch_3': 3, '140_4': 4} | {'most_greater_eq_0': 'most_greater_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'snatch_3': 'snatch', '140_4': '140'} | {'most_greater_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'snatch_3': [0], '140_4': [0]} | ['name', 'bodyweight', 'snatch', 'clean & jerk', 'total ( kg )'] | [['josé oliver ruíz ( col )', '84.45', '160.0', '203.0', '363.0'], ['jadier valladares ( cub )', '84.50', '161.0', '202.0', '363.0'], ['herbys márquez ( ven )', '84.75', '155.0', '195.0', '350.0'], ['kendrick farris ( usa )', '84.15', '158.0', '191.0', '349.0'], ['juan quiterio ( dom )', '84.35', '145.0', '185.0', '330.0'], ['buck ramsay ( can )', '84.75', '140.0', '178.0', '318.0'], ['rafael andrade ( bra )', '83.75', '140.0', '175.0', '315.0'], ['edward silva ( uru )', '84.10', '120.0', '150.0', '270.0']] |
list of counties and boroughs of the unreformed house of commons at 1800 | https://en.wikipedia.org/wiki/List_of_counties_and_boroughs_of_the_Unreformed_House_of_Commons_at_1800 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-24329520-4.html.csv | aggregation | of the counties and boroughs of the unreformed house of commons at 1800 , the average number of times contested was 2.17 . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '2.17', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'times contested'], 'result': '2.17', 'ind': 0, 'tostr': 'avg { all_rows ; times contested }'}, '2.17'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; times contested } ; 2.17 } = true', 'tointer': 'the average of the times contested record of all rows is 2.17 .'} | round_eq { avg { all_rows ; times contested } ; 2.17 } = true | the average of the times contested record of all rows is 2.17 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'times contested_4': 4, '2.17_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'times contested_4': 'times contested', '2.17_5': '2.17'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'times contested_4': [0], '2.17_5': [1]} | ['borough', 'county', 'franchise type', 'members', 'voters in 1800', 'times contested', 'fate in 1832'] | [['beaumaris', 'anglesey', 'corporation', '1', '24', '0', 'retained one seat'], ['brecon', 'brecknockshire', 'freemen', '1', '12', '0', 'retained one seat'], ['carmarthen', 'carmarthenshire', 'freemen', '1', '500', '5', 'retained one seat'], ['denbigh boroughs ( denbigh , holt , ruthin )', 'denbighshire', 'freemen', '1', '24', '4', 'retained one seat'], ['haverfordwest', 'pembrokeshire', 'scot and lot', '1', '500', '3', 'retained one seat'], ['montgomery', 'montgomeryshire', 'freemen', '1', '500', '1', 'retained one seat']] |
list of tallest buildings in nashville | https://en.wikipedia.org/wiki/List_of_tallest_buildings_in_Nashville | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12169960-1.html.csv | count | 18 buildings are included in the list of nashville 's tallest buildings . | {'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '18', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'name'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record is arbitrary .', 'tostr': 'filter_all { all_rows ; name }'}], 'result': '18', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; name } }', 'tointer': 'select the rows whose name record is arbitrary . the number of such rows is 18 .'}, '18'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; name } } ; 18 } = true', 'tointer': 'select the rows whose name record is arbitrary . the number of such rows is 18 .'} | eq { count { filter_all { all_rows ; name } } ; 18 } = true | select the rows whose name record is arbitrary . the number of such rows is 18 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'name_5': 5, '18_6': 6} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'name_5': 'name', '18_6': '18'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'name_5': [0], '18_6': [2]} | ['rank', 'name', 'height ft ( m )', 'floors', 'year'] | [['1', 'at & t building', '617 ( 188 )', '33', '1994'], ['2', 'fifth third center', '490 ( 149 )', '31', '1986'], ['3', 'william r snodgrass tennessee tower', '452 ( 138 )', '31', '1970'], ['4', 'pinnacle at symphony place', '417 ( 127 )', '28', '2010'], ['5', 'life and casualty tower', '409 ( 125 )', '30', '1957'], ['6', 'nashville city center', '402 ( 123 )', '27', '1988'], ['7', 'james k polk state office building', '392 ( 119 )', '24', '1981'], ['8', 'renaissance nashville hotel', '385 ( 117 )', '31', '1987'], ['9', 'viridian tower', '378 ( 115 )', '31', '2006'], ['10', 'one nashville place', '359 ( 109 )', '25', '1985'], ['11', 'regions center', '354 ( 108 )', '28', '1974'], ['12', 'sheraton nashville downtown', '300 ( 91 )', '27', '1975'], ['13', 'suntrust building', '292 ( 89 )', '20', '1967'], ['14', 'bank of america plaza', '292 ( 89 )', '20', '1977'], ['15', 'andrew jackson state office building', '286 ( 87 )', '17', '1969'], ['16', 'omni nashville hotel', '280 ( 85 )', '23', '2013'], ['17', 'palmer plaza', '269 ( 82 )', '18', '1986'], ['18', 'parkway towers', '261 ( 80 )', '21', '1968']] |
1987 200 miles of norisring | https://en.wikipedia.org/wiki/1987_200_Miles_of_Norisring | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16861730-2.html.csv | unique | raul boesel was the only driver with a jaguar xjr - 8 type chassis - engine in the 1987 200 miles of norisring race . | {'scope': 'all', 'row': '1', 'col': '4', 'col_other': '3', 'criterion': 'equal', 'value': 'jaguar xjr - 8', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'chassis - engine', 'jaguar xjr - 8'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose chassis - engine record fuzzily matches to jaguar xjr - 8 .', 'tostr': 'filter_eq { all_rows ; chassis - engine ; jaguar xjr - 8 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; chassis - engine ; jaguar xjr - 8 } }', 'tointer': 'select the rows whose chassis - engine record fuzzily matches to jaguar xjr - 8 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'chassis - engine', 'jaguar xjr - 8'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose chassis - engine record fuzzily matches to jaguar xjr - 8 .', 'tostr': 'filter_eq { all_rows ; chassis - engine ; jaguar xjr - 8 }'}, 'driver'], 'result': 'raul boesel', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; chassis - engine ; jaguar xjr - 8 } ; driver }'}, 'raul boesel'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; chassis - engine ; jaguar xjr - 8 } ; driver } ; raul boesel }', 'tointer': 'the driver record of this unqiue row is raul boesel .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; chassis - engine ; jaguar xjr - 8 } } ; eq { hop { filter_eq { all_rows ; chassis - engine ; jaguar xjr - 8 } ; driver } ; raul boesel } } = true', 'tointer': 'select the rows whose chassis - engine record fuzzily matches to jaguar xjr - 8 . there is only one such row in the table . the driver record of this unqiue row is raul boesel .'} | and { only { filter_eq { all_rows ; chassis - engine ; jaguar xjr - 8 } } ; eq { hop { filter_eq { all_rows ; chassis - engine ; jaguar xjr - 8 } ; driver } ; raul boesel } } = true | select the rows whose chassis - engine record fuzzily matches to jaguar xjr - 8 . there is only one such row in the table . the driver record of this unqiue row is raul boesel . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'chassis - engine_7': 7, 'jaguar xjr - 8_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'driver_9': 9, 'raul boesel_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'chassis - engine_7': 'chassis - engine', 'jaguar xjr - 8_8': 'jaguar xjr - 8', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'driver_9': 'driver', 'raul boesel_10': 'raul boesel'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'chassis - engine_7': [0], 'jaguar xjr - 8_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'driver_9': [2], 'raul boesel_10': [3]} | ['class', 'team', 'driver', 'chassis - engine', 'laps'] | [['c1', 'silk cut jaguar', 'raul boesel', 'jaguar xjr - 8', '77'], ['c1', 'liqui moly equipe', 'jonathan palmer', 'porsche 962 c', '77'], ['c1', 'brun motorsport', 'jochen mass', 'porsche 962 c', '76'], ['c1', 'joest racing', 'stanley dickens', 'porsche 962 c', '75'], ['c1', 'primagaz competition', 'pierre yver', 'porsche 962 c', '72'], ['c2', 'swiftair ecurie ecosse', 'david leslie', 'ecosse c286 - ford', '72'], ['c2', 'spice engineering', 'gordon spice', 'spice se86c - ford', '71'], ['c2', 'tiga ford denmark', 'john sheldon', 'tiga gc287 - ford', '70'], ['c2', 'spice engineering', 'nick adams', 'spice se87c - ford', '70'], ['c1', 'brun motorsport', 'jésus pareja', 'porsche 962 c', '70'], ['c2', 'kelmar racing', 'ranieri randaccio', 'tiga gc85 - ford', '69'], ['c2', 'schanche racing', 'martin schanche', 'argo jm19b - zakspeed', '64'], ['c1', 'porsche kremer racing', 'kris nissen', 'porsche 962 c', '75'], ['c1', 'blaupunkt joest racing', 'klaus ludwig', 'porsche 962 c', '77'], ['c1', 'porsche ag', 'derek bell', 'porsche 962 c', '61'], ['c2', 'swiftair ecurie ecosse', 'mike wilds', 'ecosse c286 - ford', '25']] |
german submarine u - 404 | https://en.wikipedia.org/wiki/German_submarine_U-404 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17794265-1.html.csv | count | two ships were only damaged when attacked by the german u 404 . | {'scope': 'all', 'criterion': 'equal', 'value': 'damaged', 'result': '2', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'fate', 'damaged'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose fate record fuzzily matches to damaged .', 'tostr': 'filter_eq { all_rows ; fate ; damaged }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; fate ; damaged } }', 'tointer': 'select the rows whose fate record fuzzily matches to damaged . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; fate ; damaged } } ; 2 } = true', 'tointer': 'select the rows whose fate record fuzzily matches to damaged . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; fate ; damaged } } ; 2 } = true | select the rows whose fate record fuzzily matches to damaged . 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, 'fate_5': 5, 'damaged_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', 'fate_5': 'fate', 'damaged_6': 'damaged', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'fate_5': [0], 'damaged_6': [0], '2_7': [2]} | ['date', 'ship', 'nationality', 'tonnage', 'fate'] | [['5 march 1942', 'collamer', 'usa', '5112', 'sunk'], ['13 march 1942', 'tolten', 'chile', '1858', 'sunk'], ['14 march 1942', 'lemuel burrows', 'usa', '7610', 'sunk'], ['17 march 1942', 'san demitro', 'great britain', '8073', 'sunk'], ['30 may 1942', 'aloca shipper', 'usa', '5491', 'sunk'], ['1 june 1942', 'west notus', 'usa', '5492', 'sunk'], ['3 june 1942', 'anna', 'sweden', '1345', 'sunk'], ['24 june 1942', 'ljubica matokovic', 'yugoslavia', '3289', 'sunk'], ['25 june 1942', 'manuda', 'usa', '4772', 'sunk'], ['25 june 1942', 'nordal', 'panama', '3845', 'sunk'], ['27 june 1942', 'moldanger', 'norway', '6827', 'sunk'], ['11 september 1942', 'marit ii', 'norway', '7141', 'damaged'], ['12 september 1942', 'daghild', 'norway', '9272', 'damaged'], ['26 september 1942', 'hms veteran', 'great britain', '1120', 'sunk'], ['29 march 1943', 'nagara', 'great britain', '8791', 'sunk'], ['30 march 1943', 'empire bowman', 'great britain', '7031', 'sunk'], ['12 april 1943', 'lancastrian prince', 'great britain', '1914', 'sunk']] |
orlando magic all - time roster | https://en.wikipedia.org/wiki/Orlando_Magic_all-time_roster | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15621965-10.html.csv | majority | most of the players in the roster are from the united states . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'united states', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'nationality', 'united states'], 'result': True, 'ind': 0, 'tointer': 'for the nationality records of all rows , most of them fuzzily match to united states .', 'tostr': 'most_eq { all_rows ; nationality ; united states } = true'} | most_eq { all_rows ; nationality ; united states } = true | for the nationality records of all rows , most of them fuzzily match to united states . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'nationality_3': 3, 'united states_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'nationality_3': 'nationality', 'united states_4': 'united states'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'nationality_3': [0], 'united states_4': [0]} | ['player', 'no', 'nationality', 'position', 'years in orlando', 'school / club team'] | [['mario kasun', '41', 'croatia', 'center', '2004 - 2006', 'gonzaga'], ['shawn kemp', '40', 'united states', 'forward', '2002 - 2003', 'concord hs'], ['tim kempton', '9', 'united states', 'forward - center', '2002 - 2004', 'notre dame'], ['jonathan kerner', '52', 'united states', 'center', '1998 - 1999', 'east carolina'], ['steve kerr', '2', 'united states', 'guard', '1992 - 1993', 'arizona'], ['greg kite', '34', 'united states', 'center', '1990 - 1994', 'byu'], ['jon koncak', '45', 'united states', 'center', '1995 - 1996', 'southern methodist']] |
color in chinese culture | https://en.wikipedia.org/wiki/Color_in_Chinese_culture | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15305217-1.html.csv | unique | only wood is associated with the color green in chinese culture . | {'scope': 'all', 'row': '1', 'col': '2', 'col_other': 'n/a', 'criterion': 'equal', 'value': 'green', 'subset': None} | {'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'wood', 'green'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose wood record fuzzily matches to green .', 'tostr': 'filter_eq { all_rows ; wood ; green }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; wood ; green } } = true', 'tointer': 'select the rows whose wood record fuzzily matches to green . there is only one such row in the table .'} | only { filter_eq { all_rows ; wood ; green } } = true | select the rows whose wood record fuzzily matches to green . there is only one such row in the table . | 2 | 2 | {'only_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'wood_4': 4, 'green_5': 5} | {'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'wood_4': 'wood', 'green_5': 'green'} | {'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'wood_4': [0], 'green_5': [0]} | ['element', 'wood', 'fire', 'earth', 'metal', 'water'] | [['color', 'green', 'red', 'yellow', 'white', 'black'], ['direction', 'east', 'south', 'center', 'west', 'north'], ['planet', 'jupiter', 'mars', 'saturn', 'venus', 'mercury'], ['heavenly creature', 'azure dragon 青龍', 'vermilion bird 朱雀', 'yellow dragon 黃龍', 'white tiger 白虎', 'black tortoise 玄武'], ['heavenly stems', '甲 , 乙', '丙 , 丁', '戊 , 己', '庚 , 辛', '壬 , 癸'], ['phase', 'new yang', 'full yang', 'yin / yang balance', 'new yin', 'full yin'], ['energy', 'generative', 'expansive', 'stabilizing', 'contracting', 'conserving'], ['season', 'spring', 'summer', 'change of seasons ( every third month )', 'autumn', 'winter'], ['climate', 'windy', 'hot', 'damp', 'dry', 'cold'], ['development', 'sprouting', 'blooming', 'ripening', 'withering', 'dormant'], ['livestock', 'dog', 'sheep / goat', 'cattle', 'chicken', 'pig'], ['fruit', 'plum', 'apricot', 'jujube', 'peach', 'chestnut'], ['grain', 'wheat', 'beans', 'rice', 'hemp', 'millet']] |
united states house of representatives elections , 1994 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1994 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341522-24.html.csv | majority | in the 1994 united states house of representatives election , all of the incumbents were re-elected . | {'scope': 'all', 'col': '5', 'most_or_all': 'all', 'criterion': 'equal', 'value': 're-elected', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'status', 're-elected'], 'result': True, 'ind': 0, 'tointer': 'for the status records of all rows , all of them fuzzily match to re-elected .', 'tostr': 'all_eq { all_rows ; status ; re-elected } = true'} | all_eq { all_rows ; status ; re-elected } = true | for the status records of all rows , all of them fuzzily match to re-elected . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'status_3': 3, 're-elected_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'status_3': 'status', 're-elected_4': 're-elected'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'status_3': [0], 're-elected_4': [0]} | ['district', 'incumbent', 'party', 'first elected', 'status', 'opponent'] | [['massachusetts1', 'john olver', 'democratic', '1991', 're - elected', 'john olver ( d ) unopposed'], ['massachusetts4', 'barney frank', 'democratic', '1980', 're - elected', 'barney frank ( d ) unopposed'], ['massachusetts5', 'marty meehan', 'democratic', '1992', 're - elected', 'marty meehan ( d ) 69.8 % david e coleman ( r ) 30.1 %'], ['massachusetts7', 'ed markey', 'democratic', '1976', 're - elected', 'ed markey ( d ) 64.4 % brad bailey ( r ) 35.5 %'], ['massachusetts8', 'joe kennedy', 'democratic', '1986', 're - elected', 'joe kennedy ( d ) unopposed']] |
the evian championship | https://en.wikipedia.org/wiki/The_Evian_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1529260-3.html.csv | ordinal | the second time laura davies was the champion of the evian championship , the margin of victory was 4 strokes . | {'scope': 'subset', 'row': '4', 'col': '1', 'order': '2', 'col_other': '3,7', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'laura davies'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'champion', 'laura davies'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; champion ; laura davies }', 'tointer': 'select the rows whose champion record fuzzily matches to laura davies .'}, 'year', '2'], 'result': None, 'ind': 1, 'tostr': 'nth_argmin { filter_eq { all_rows ; champion ; laura davies } ; year ; 2 }'}, 'margin of victory'], 'result': '4 strokes', 'ind': 2, 'tostr': 'hop { nth_argmin { filter_eq { all_rows ; champion ; laura davies } ; year ; 2 } ; margin of victory }'}, '4 strokes'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmin { filter_eq { all_rows ; champion ; laura davies } ; year ; 2 } ; margin of victory } ; 4 strokes } = true', 'tointer': 'select the rows whose champion record fuzzily matches to laura davies . select the row whose year record of these rows is 2nd minimum . the margin of victory record of this row is 4 strokes .'} | eq { hop { nth_argmin { filter_eq { all_rows ; champion ; laura davies } ; year ; 2 } ; margin of victory } ; 4 strokes } = true | select the rows whose champion record fuzzily matches to laura davies . select the row whose year record of these rows is 2nd minimum . the margin of victory record of this row is 4 strokes . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmin_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'champion_6': 6, 'laura davies_7': 7, 'year_8': 8, '2_9': 9, 'margin of victory_10': 10, '4 strokes_11': 11} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmin_1': 'nth_argmin', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'champion_6': 'champion', 'laura davies_7': 'laura davies', 'year_8': 'year', '2_9': '2', 'margin of victory_10': 'margin of victory', '4 strokes_11': '4 strokes'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmin_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'champion_6': [0], 'laura davies_7': [0], 'year_8': [1], '2_9': [1], 'margin of victory_10': [2], '4 strokes_11': [3]} | ['year', 'dates', 'champion', 'country', 'score', 'to par', 'margin of victory'] | [['1999', 'jun 9 - 12', 'catrin nilsmark', 'sweden', '69 + 70 + 72 + 68 = 279', '- 9', '2 strokes'], ['1998', 'jun 3 - 6', 'helen alfredsson', 'sweden', '70 + 69 + 73 + 65 = 277', '- 11', '4 strokes'], ['1997', 'jun 18 - 21', 'hiromi kobayashi', 'japan', '69 + 67 + 69 + 69 = 274', '- 14', 'playoff'], ['1996', 'jun 19 - 22', 'laura davies', 'england', '72 + 69 + 65 + 68 = 274', '- 14', '4 strokes'], ['1995', 'jun 7 - 10', 'laura davies', 'england', '68 + 67 + 69 + 67 = 271', '- 17', '5 strokes'], ['1994', 'jun 9 - 12', 'helen alfredsson', 'sweden', '71 + 73 + 73 + 70 = 287', '- 1', '3 strokes']] |
list of sri lanka one day international cricket records | https://en.wikipedia.org/wiki/List_of_Sri_Lanka_One_Day_International_cricket_records | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26041144-11.html.csv | count | for the sri lanka one day international cricket records , when there are over 200 matches , there were 3 players with over 250 innings . | {'scope': 'subset', 'criterion': 'greater_than', 'value': '250', 'result': '3', 'col': '5', 'subset': {'col': '4', 'criterion': 'greater_than', 'value': '200'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'matches', '200'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; matches ; 200 }', 'tointer': 'select the rows whose matches record is greater than 200 .'}, 'innings', '250'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose matches record is greater than 200 . among these rows , select the rows whose innings record is greater than 250 .', 'tostr': 'filter_greater { filter_greater { all_rows ; matches ; 200 } ; innings ; 250 }'}], 'result': '3', 'ind': 2, 'tostr': 'count { filter_greater { filter_greater { all_rows ; matches ; 200 } ; innings ; 250 } }', 'tointer': 'select the rows whose matches record is greater than 200 . among these rows , select the rows whose innings record is greater than 250 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_greater { filter_greater { all_rows ; matches ; 200 } ; innings ; 250 } } ; 3 } = true', 'tointer': 'select the rows whose matches record is greater than 200 . among these rows , select the rows whose innings record is greater than 250 . the number of such rows is 3 .'} | eq { count { filter_greater { filter_greater { all_rows ; matches ; 200 } ; innings ; 250 } } ; 3 } = true | select the rows whose matches record is greater than 200 . among these rows , select the rows whose innings record is greater than 250 . the number of such rows is 3 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_greater_1': 1, 'filter_greater_0': 0, 'all_rows_5': 5, 'matches_6': 6, '200_7': 7, 'innings_8': 8, '250_9': 9, '3_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_greater_1': 'filter_greater', 'filter_greater_0': 'filter_greater', 'all_rows_5': 'all_rows', 'matches_6': 'matches', '200_7': '200', 'innings_8': 'innings', '250_9': '250', '3_10': '3'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_greater_1': [2], 'filter_greater_0': [1], 'all_rows_5': [0], 'matches_6': [0], '200_7': [0], 'innings_8': [1], '250_9': [1], '3_10': [3]} | ['rank', 'average', 'player', 'matches', 'innings', 'period'] | [['1', '39.69', 'kumar sangakkara', '351', '328', '2000 - pre'], ['2', '37.57', 'marvan atapattu', '268', '259', '1990 - 2007'], ['3', '36.74', 'tillakaratne dilshan', '264', '239', '1999 - pre'], ['4', '35.84', 'arjuna ranatunga', '269', '255', '1982 - 1999'], ['5', '35.26', 'russel arnold', '180', '155', '1997 - 2007'], ['6', '34.13', 'upul tharanga', '169', '162', '2005 - pre'], ['7', '34.08', 'angelo mathews', '102', '82', '2008 - pre']] |
ireland in the eurovision song contest 1980 | https://en.wikipedia.org/wiki/Ireland_in_the_Eurovision_Song_Contest_1980 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18682634-1.html.csv | superlative | in the 1980 eurovision song contest , ireland 's song what 's another year won first place . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '5', 'value_mentioned': 'yes', 'max_or_min': 'min', 'other_col': '2', 'subset': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'min', 'args': ['all_rows', 'place'], 'result': '1st', 'ind': 0, 'tostr': 'min { all_rows ; place }', 'tointer': 'the minimum place record of all rows is 1st .'}, '1st'], 'result': True, 'ind': 1, 'tostr': 'eq { min { all_rows ; place } ; 1st }', 'tointer': 'the minimum place record of all rows is 1st .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'place'], 'result': None, 'ind': 2, 'tostr': 'argmin { all_rows ; place }'}, 'song'], 'result': "what 's another year", 'ind': 3, 'tostr': 'hop { argmin { all_rows ; place } ; song }'}, "what 's another year"], 'result': True, 'ind': 4, 'tostr': "eq { hop { argmin { all_rows ; place } ; song } ; what 's another year }", 'tointer': "the song record of the row with superlative place record is what 's another year ."}], 'result': True, 'ind': 5, 'tostr': "and { eq { min { all_rows ; place } ; 1st } ; eq { hop { argmin { all_rows ; place } ; song } ; what 's another year } } = true", 'tointer': "the minimum place record of all rows is 1st . the song record of the row with superlative place record is what 's another year ."} | and { eq { min { all_rows ; place } ; 1st } ; eq { hop { argmin { all_rows ; place } ; song } ; what 's another year } } = true | the minimum place record of all rows is 1st . the song record of the row with superlative place record is what 's another year . | 6 | 6 | {'and_5': 5, 'result_6': 6, 'eq_1': 1, 'min_0': 0, 'all_rows_7': 7, 'place_8': 8, '1st_9': 9, 'str_eq_4': 4, 'str_hop_3': 3, 'argmin_2': 2, 'all_rows_10': 10, 'place_11': 11, 'song_12': 12, "what 's another year_13": 13} | {'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'min_0': 'min', 'all_rows_7': 'all_rows', 'place_8': 'place', '1st_9': '1st', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'argmin_2': 'argmin', 'all_rows_10': 'all_rows', 'place_11': 'place', 'song_12': 'song', "what 's another year_13": "what 's another year"} | {'and_5': [6], 'result_6': [], 'eq_1': [5], 'min_0': [1], 'all_rows_7': [0], 'place_8': [0], '1st_9': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'argmin_2': [3], 'all_rows_10': [2], 'place_11': [2], 'song_12': [3], "what 's another year_13": [4]} | ['draw', 'song', 'artist', 'points', 'place'] | [['1', "loving wo n't let you down", 'roy taylor & karen black', '13', '3rd'], ['2', 'take me back again', 'the straw hat and garter company', '2', '8th'], ['3', 'the saddest show on earth', 'eileen reid', '10', '4th'], ['4', "you 're so cheeky", 'charlie chapman & the miami', '5', '5th'], ['5', "what 's another year", 'johnny logan', '40', '1st'], ['6', 'you have', 'the dajacs', '4', '7th'], ['7', 'stepping stones', 'peter beckett', '21', '2nd'], ['8', 'love is all there is', 'romance', '5', '5th']] |
2008 chicago sky season | https://en.wikipedia.org/wiki/2008_Chicago_Sky_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17118657-10.html.csv | unique | chicago 's game on september 12 is the only that recorded more than one player with high rebounds . | {'scope': 'all', 'row': '5', 'col': '6', 'col_other': '2', 'criterion': 'fuzzily_match', 'value': ',', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'high rebounds', ','], 'result': None, 'ind': 0, 'tointer': 'select the rows whose high rebounds record fuzzily matches to , .', 'tostr': 'filter_eq { all_rows ; high rebounds ; , }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; high rebounds ; , } }', 'tointer': 'select the rows whose high rebounds record fuzzily matches to , . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'high rebounds', ','], 'result': None, 'ind': 0, 'tointer': 'select the rows whose high rebounds record fuzzily matches to , .', 'tostr': 'filter_eq { all_rows ; high rebounds ; , }'}, 'date'], 'result': 'september 12', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; high rebounds ; , } ; date }'}, 'september 12'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; high rebounds ; , } ; date } ; september 12 }', 'tointer': 'the date record of this unqiue row is september 12 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; high rebounds ; , } } ; eq { hop { filter_eq { all_rows ; high rebounds ; , } ; date } ; september 12 } } = true', 'tointer': 'select the rows whose high rebounds record fuzzily matches to , . there is only one such row in the table . the date record of this unqiue row is september 12 .'} | and { only { filter_eq { all_rows ; high rebounds ; , } } ; eq { hop { filter_eq { all_rows ; high rebounds ; , } ; date } ; september 12 } } = true | select the rows whose high rebounds record fuzzily matches to , . there is only one such row in the table . the date record of this unqiue row is september 12 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'high rebounds_7': 7, ',_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, 'september 12_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'high rebounds_7': 'high rebounds', ',_8': ',', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', 'september 12_10': 'september 12'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'high rebounds_7': [0], ',_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], 'september 12_10': [3]} | ['game', 'date', 'opponent', 'score', 'high points', 'high rebounds', 'high assists', 'location / attendance', 'record'] | [['29', 'september 4', 'seattle', '62 - 70', 'perkins ( 22 )', 'dupree ( 6 )', 'canty ( 6 )', 'uic pavilion 3829', '11 - 18'], ['30', 'september 5', 'connecticut', '75 - 80', 'perkins ( 18 )', 'fowles ( 6 )', 'canty ( 7 )', 'mohegan sun arena 8088', '11 - 19'], ['31', 'september 7', 'new york', '61 - 69', 'perkins ( 18 )', 'fowles ( 12 )', 'canty , sharp ( 2 )', 'madison square garden 7903', '11 - 20'], ['32', 'september 9', 'washington', '78 - 59', 'perkins ( 17 )', 'dupree ( 10 )', 'dupree ( 6 )', 'uic pavilion 3087', '12 - 20'], ['33', 'september 12', 'new york', '62 - 69', 'dupree ( 18 )', 'dupree , fowles ( 6 )', 'canty , wyckoff ( 4 )', 'uic pavilion 5681', '12 - 21'], ['34', 'september 14', 'houston', '76 - 79', 'dupree ( 20 )', 'price ( 7 )', 'canty ( 6 )', 'uic pavilion 4917', '12 - 22']] |
2007 japanese television dramas | https://en.wikipedia.org/wiki/2007_Japanese_television_dramas | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18539861-3.html.csv | aggregation | the 2007 japanese television dramas drew an average viewership rating of 11.76 % . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '11.76 %', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'average ratings'], 'result': '11.76 %', 'ind': 0, 'tostr': 'avg { all_rows ; average ratings }'}, '11.76 %'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; average ratings } ; 11.76 % } = true', 'tointer': 'the average of the average ratings record of all rows is 11.76 % .'} | round_eq { avg { all_rows ; average ratings } ; 11.76 % } = true | the average of the average ratings record of all rows is 11.76 % . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'average ratings_4': 4, '11.76%_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'average ratings_4': 'average ratings', '11.76%_5': '11.76 %'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'average ratings_4': [0], '11.76%_5': [1]} | ['japanese title', 'romaji title', 'tv station', 'episodes', 'average ratings'] | [['スシ王子 ！', 'sushi ouji !', 'tv asahi', '8', '7.5 %'], ['菊次郎とさき 3', 'kikujirou to saki 3', 'tv asahi', '11', '9.3 %'], ['牛に願いを love & farm', 'ushi ni negai wo - love & farm', 'fuji tv', '11', '8.7 %'], ['ライフ', 'life', 'fuji tv', '11', '12.16 %'], ['受験の神様', 'juken no kamisama', 'ntv', '9', '9.5 %'], ['パパとムスメの7日間', 'papa to musume no nanokakan', 'tbs', '7', '13.9 %'], ['肩ごしの恋人', 'katagoshi no koibito', 'tbs', '9', '7.4 %'], ['花ざかりの君たちへ ～ イケメン ♂ パラダイス ～', 'hanazakari no kimitachi e ～ ikemen ♂ paradise ～', 'fuji tv', '12', '17.04 %'], ['ファースト ・ キス', 'first kiss', 'fuji tv', '11', '14.1 %'], ['山おんな壁おんな', 'yama onna kabe onna', 'fuji tv', '12', '12.1 %'], ['ホタルノヒカリ', 'hotaru no hikari', 'ntv', '10', '13.6 %'], ['山田太郎ものがたり', 'yamada taro monogatari', 'tbs', '10', '15.24 %'], ['探偵学園q', 'tantei gakuen q', 'ntv', '11', '11.1 %'], ['地獄の沙汰もヨメ次第', 'jigoku no sada mo yome shidai', 'tbs', '10', '10.3 %'], ['女帝', 'jotei', 'tv asahi', '10', '14.4 %']] |
2008 pga tour | https://en.wikipedia.org/wiki/2008_PGA_Tour | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14473512-2.html.csv | aggregation | players in the 2008 pga tour golf series won an average prize money of 4831665 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '4831665', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'prize money'], 'result': '4831665', 'ind': 0, 'tostr': 'avg { all_rows ; prize money }'}, '4831665'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; prize money } ; 4831665 } = true', 'tointer': 'the average of the prize money record of all rows is 4831665 .'} | round_eq { avg { all_rows ; prize money } ; 4831665 } = true | the average of the prize money record of all rows is 4831665 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'prize money_4': 4, '4831665_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'prize money_4': 'prize money', '4831665_5': '4831665'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'prize money_4': [0], '4831665_5': [1]} | ['rank', 'player', 'country', 'events', 'prize money'] | [['1', 'vijay singh', 'fiji', '23', '6601094'], ['2', 'tiger woods', 'united states', '6', '5775000'], ['3', 'phil mickelson', 'united states', '21', '5118875'], ['4', 'sergio garcía', 'spain', '19', '4858224'], ['5', 'kenny perry', 'united states', '26', '4663794'], ['6', 'anthony kim', 'united states', '22', '4656265'], ['7', 'camilo villegas', 'colombia', '22', '4422641'], ['8', 'pádraig harrington', 'ireland', '15', '4313551'], ['9', 'stewart cink', 'united states', '22', '3963661'], ['10', 'justin leonard', 'united states', '25', '3943542']] |
acc - big ten challenge | https://en.wikipedia.org/wiki/ACC%E2%80%93Big_Ten_Challenge | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1672976-6.html.csv | superlative | the acc - big ten challenge game that was played at kohl center madison , wi had the largest attendance . | {'scope': 'all', 'col_superlative': '7', 'row_superlative': '8', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '5', '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 }'}, 'location'], 'result': 'kohl center madison , wi', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; attendance } ; location }'}, 'kohl center madison , wi'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; location } ; kohl center madison , wi } = true', 'tointer': 'select the row whose attendance record of all rows is maximum . the location record of this row is kohl center madison , wi .'} | eq { hop { argmax { all_rows ; attendance } ; location } ; kohl center madison , wi } = true | select the row whose attendance record of all rows is maximum . the location record of this row is kohl center madison , wi . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, 'location_6': 6, 'kohl center madison , wi_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', 'location_6': 'location', 'kohl center madison , wi_7': 'kohl center madison , wi'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], 'location_6': [1], 'kohl center madison , wi_7': [2]} | ['date', 'time', 'acc team', 'big ten team', 'location', 'television', 'attendance', 'winner', 'challenge leader'] | [['mon , nov 29', '7:00 pm', 'virginia', '13 minnesota', 'williams arena minneapolis , mn', 'espn2', '12089', 'virginia ( 87 - 79 )', 'acc ( 1 - 0 )'], ['tue , nov 30', '7:00 pm', 'wake forest', 'iowa', 'ljvm coliseum winston - salem , nc', 'espnu', '9086', 'wake forest ( 76 - 73 )', 'acc ( 2 - 0 )'], ['tue , nov 30', '7:00 pm', 'georgia tech', 'northwestern', 'welsh - ryan arena evanston , il', 'espn2', '4455', 'northwestern ( 91 - 71 )', 'acc ( 2 - 1 )'], ['tue , nov 30', '7:30 pm', 'florida state', '2 ohio state', 'donald l tucker center tallahassee , fl', 'espn', '10457', 'ohio state ( 58 - 44 )', 'tied ( 2 - 2 )'], ['tue , nov 30', '9:00 pm', 'clemson', 'michigan', 'littlejohn coliseum clemson , sc', 'espn2', '7237', 'michigan ( 69 - 61 )', 'big ten ( 3 - 2 )'], ['tue , nov 30', '9:30 pm', 'north carolina', '21 illinois', 'assembly hall champaign , il', 'espn', '16618', 'illinois ( 79 - 67 )', 'big ten ( 4 - 2 )'], ['wed , dec 1', '7:15 pm', 'boston college', 'indiana', 'conte forum chestnut hill , ma', 'espnu', '5329', 'boston college ( 88 - 76 )', 'big ten ( 4 - 3 )'], ['wed , dec 1', '7:15 pm', 'nc state', 'wisconsin', 'kohl center madison , wi', 'espn2', '17230', 'wisconsin ( 87 - 48 )', 'big ten ( 5 - 3 )'], ['wed , dec 1', '7:30 pm', 'virginia tech', '18 purdue', 'cassell coliseum blacksburg , va', 'espn', '9847', 'purdue ( 58 - 55 ot )', 'big ten ( 6 - 3 )'], ['wed , dec 1', '9:15 pm', 'maryland', 'penn state', 'bryce jordan center university park , pa', 'espn2', '9078', 'maryland ( 62 - 39 )', 'big ten ( 6 - 4 )']] |
christian vietoris | https://en.wikipedia.org/wiki/Christian_Vietoris | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-10705060-1.html.csv | superlative | the 2006 race was the only one christian vietoris finished in 1st in his career between 2005 and 2012 . | {'scope': 'all', 'col_superlative': '8', 'row_superlative': '2', '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', 'position'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; position }'}, 'season'], 'result': '2006', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; position } ; season }'}, '2006'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; position } ; season } ; 2006 } = true', 'tointer': 'select the row whose position record of all rows is minimum . the season record of this row is 2006 .'} | eq { hop { argmin { all_rows ; position } ; season } ; 2006 } = true | select the row whose position record of all rows is minimum . the season record of this row is 2006 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'position_5': 5, 'season_6': 6, '2006_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'position_5': 'position', 'season_6': 'season', '2006_7': '2006'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'position_5': [0], 'season_6': [1], '2006_7': [2]} | ['season', 'series', 'team name', 'races', 'poles', 'wins', 'points', 'position'] | [['2005', 'formula bmw adac', 'eifelland racing', '19', '0', '0', '17', '16th'], ['2006', 'formula bmw adac', 'josef kaufmann racing', '18', '9', '9', '277', '1st'], ['2007', 'german formula three', 'josef kaufmann racing', '12', '2', '1', '62', '6th'], ['2008', 'formula 3 euro series', 'mücke motorsport', '20', '1', '1', '36', '6th'], ['2009', 'formula 3 euro series', 'mücke motorsport', '18', '0', '4', '75', '2nd'], ['2009 - 10', 'gp2 asia series', 'dams', '8', '0', '1', '9', '10th'], ['2010', 'gp2 series', 'racing engineering', '18', '0', '1', '29', '9th'], ['2011', 'gp2 series', 'racing engineering', '14', '1', '2', '35', '7th'], ['2011', 'deutsche tourenwagen masters', 'persson motorsport', '10', '0', '0', '4', '14th'], ['2012', 'deutsche tourenwagen masters', 'hwa team', '10', '0', '0', '25', '12th']] |
2005 chicago white sox season | https://en.wikipedia.org/wiki/2005_Chicago_White_Sox_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12569321-11.html.csv | comparative | the chicago white sox game played on october 15 had a longer time than the game played on october 12 . | {'row_1': '4', 'row_2': '2', 'col': '5', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'october 15'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to october 15 .', 'tostr': 'filter_eq { all_rows ; date ; october 15 }'}, 'time'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; october 15 } ; time }', 'tointer': 'select the rows whose date record fuzzily matches to october 15 . take the time record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'october 12'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to october 12 .', 'tostr': 'filter_eq { all_rows ; date ; october 12 }'}, 'time'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; october 12 } ; time }', 'tointer': 'select the rows whose date record fuzzily matches to october 12 . take the time record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; date ; october 15 } ; time } ; hop { filter_eq { all_rows ; date ; october 12 } ; time } } = true', 'tointer': 'select the rows whose date record fuzzily matches to october 15 . take the time record of this row . select the rows whose date record fuzzily matches to october 12 . take the time record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; date ; october 15 } ; time } ; hop { filter_eq { all_rows ; date ; october 12 } ; time } } = true | select the rows whose date record fuzzily matches to october 15 . take the time record of this row . select the rows whose date record fuzzily matches to october 12 . take the time 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, 'date_7': 7, 'october 15_8': 8, 'time_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'date_11': 11, 'october 12_12': 12, 'time_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', 'date_7': 'date', 'october 15_8': 'october 15', 'time_9': 'time', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'date_11': 'date', 'october 12_12': 'october 12', 'time_13': 'time'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'date_7': [0], 'october 15_8': [0], 'time_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'date_11': [1], 'october 12_12': [1], 'time_13': [3]} | ['date', 'opponent', 'score', 'loss', 'time', 'att', 'record'] | [['october 11', 'angels', '2 - 3', 'contreras ( 1 - 1 )', '2:47', '40659', '3 - 1 ( 0 - 1 )'], ['october 12', 'angels', '2 - 1', 'escobar ( 1 - 1 )', '2:34', '41013', '4 - 1 ( 1 - 1 )'], ['october 14', 'angels', '5 - 2', 'lackey ( 0 - 1 )', '2:42', '44725', '5 - 1 ( 2 - 1 )'], ['october 15', 'angels', '8 - 2', 'santana ( 1 - 1 )', '2:46', '44857', '6 - 1 ( 3 - 1 )'], ['october 16', 'angels', '6 - 3', 'escobar ( 1 - 2 )', '3:11', '44712', '7 - 1 ( 4 - 1 )']] |
cycling at the 2008 summer olympics - men 's bmx | https://en.wikipedia.org/wiki/Cycling_at_the_2008_Summer_Olympics_%E2%80%93_Men%27s_BMX | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18603914-3.html.csv | comparative | marc willers had a larger first run than mike day . | {'row_1': '2', 'row_2': '1', 'col': '3', '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', 'name', 'marc willers ( nzl )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to marc willers ( nzl ) .', 'tostr': 'filter_eq { all_rows ; name ; marc willers ( nzl ) }'}, '1st run'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; marc willers ( nzl ) } ; 1st run }', 'tointer': 'select the rows whose name record fuzzily matches to marc willers ( nzl ) . take the 1st run record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'mike day ( usa )'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to mike day ( usa ) .', 'tostr': 'filter_eq { all_rows ; name ; mike day ( usa ) }'}, '1st run'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; mike day ( usa ) } ; 1st run }', 'tointer': 'select the rows whose name record fuzzily matches to mike day ( usa ) . take the 1st run record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; name ; marc willers ( nzl ) } ; 1st run } ; hop { filter_eq { all_rows ; name ; mike day ( usa ) } ; 1st run } } = true', 'tointer': 'select the rows whose name record fuzzily matches to marc willers ( nzl ) . take the 1st run record of this row . select the rows whose name record fuzzily matches to mike day ( usa ) . take the 1st run record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; name ; marc willers ( nzl ) } ; 1st run } ; hop { filter_eq { all_rows ; name ; mike day ( usa ) } ; 1st run } } = true | select the rows whose name record fuzzily matches to marc willers ( nzl ) . take the 1st run record of this row . select the rows whose name record fuzzily matches to mike day ( usa ) . take the 1st run 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, 'name_7': 7, 'marc willers ( nzl )_8': 8, '1st run_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'name_11': 11, 'mike day ( usa )_12': 12, '1st run_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', 'name_7': 'name', 'marc willers ( nzl )_8': 'marc willers ( nzl )', '1st run_9': '1st run', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'name_11': 'name', 'mike day ( usa )_12': 'mike day ( usa )', '1st run_13': '1st run'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'name_7': [0], 'marc willers ( nzl )_8': [0], '1st run_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'name_11': [1], 'mike day ( usa )_12': [1], '1st run_13': [3]} | ['rank', 'name', '1st run', '2nd run', '3rd run', 'total'] | [['1', 'mike day ( usa )', '36.170 ( 1 )', '36.080 ( 1 )', '36.122 ( 1 )', '3'], ['2', 'marc willers ( nzl )', '47.614 ( 4 )', '36.253 ( 3 )', '36.278 ( 2 )', '9'], ['3', 'donny robinson ( usa )', '48.906 ( 6 )', '36.235 ( 2 )', '36.490 ( 3 )', '11'], ['4', 'andrés jiménez caicedo ( col )', '36.619 ( 2 )', '36.939 ( 5 )', '36.660 ( 4 )', '11'], ['5', 'jonathan suárez ( ven )', '53.614 ( 8 )', '36.481 ( 4 )', '36.789 ( 5 )', '17'], ['6', 'emilio falla ( ecu )', '37.080 ( 3 )', '37.381 ( 6 )', '1:02.877 ( 8 )', '17'], ['7', 'akifumi sakamoto ( jpn )', '48.487 ( 5 )', '42.614 ( 8 )', '40.046 ( 6 )', '19'], ['8', 'ivo lakučs ( lat )', '53.300 ( 7 )', '39.213 ( 7 )', '57.461 ( 7 )', '21']] |
2007 - 08 four hills tournament | https://en.wikipedia.org/wiki/2007%E2%80%9308_Four_Hills_Tournament | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14948647-1.html.csv | aggregation | the average of the total points during the 2007 – 08 four hills tournament was 962.2 . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '962.2', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'total points'], 'result': '962.2', 'ind': 0, 'tostr': 'avg { all_rows ; total points }'}, '962.2'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; total points } ; 962.2 } = true', 'tointer': 'the average of the total points record of all rows is 962.2 .'} | round_eq { avg { all_rows ; total points } ; 962.2 } = true | the average of the total points record of all rows is 962.2 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'total points_4': 4, '962.2_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'total points_4': 'total points', '962.2_5': '962.2'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'total points_4': [0], '962.2_5': [1]} | ['rank', 'name', 'nationality', 'total points', 'oberstdorf ( rk )', 'ga - pa ( rk )', 'bhofen1 ( rk )', 'bhofen2 ( rk )'] | [['1', 'janne ahonen', 'fin', '1085.8', '279.0 ( 3 )', '272.7 ( 2 )', '282.5 ( 1 )', '251.6 ( 1 )'], ['2', 'thomas morgenstern', 'aut', '1066.0', '295.9 ( 1 )', '256.0 ( 9 )', '271.4 ( 2 )', '242.7 ( 3 )'], ['3', 'michael neumayer', 'ger', '994.6', '259.5 ( 7 )', '258.6 ( 3 )', '249.9 ( 7 )', '226.9 ( 10 )'], ['4', 'adam małysz', 'pol', '979.9', '246.9 ( 17 )', '258.6 ( 5 )', '244.3 ( 9 )', '232.1 ( 6 )'], ['5', 'dmitry vassiliev', 'rus', '977.5', '248.3 ( 13 )', '240.2 ( 13 )', '257.1 ( 4 )', '231.9 ( 7 )'], ['6', 'andreas küttel', 'sui', '959.3', '253.0 ( 10 )', '253.2 ( 7 )', '244.3 ( 9 )', '208.8 ( 25 )'], ['7', 'anders bardal', 'nor', '958.7', '243.4 ( 18 )', '245.1 ( 11 )', '226.6 ( 19 )', '243.6 ( 2 )'], ['8', 'martin schmitt', 'ger', '955.9', '252.6 ( 11 )', '227.5 ( 19 )', '240.1 ( 11 )', '235.7 ( 4 )'], ['9', 'anders jacobsen', 'nor', '943.2', '258.3 ( 8 )', '233.4 ( 16 )', '220.3 ( 23 )', '231.2 ( 8 )'], ['10', 'janne happonen', 'fin', '936.6', '252.5 ( 12 )', '228.5 ( 18 )', '232.1 ( 14 )', '223.5 ( 12 )'], ['11', 'roman koudelka', 'cze', '932.4', '247.5 ( 16 )', '256.7 ( 4 )', '202.4 ( 30 )', '225.8 ( 11 )'], ['12', 'gregor schlierenzauer', 'aut', '902.3', '280.7 ( 2 )', '274.4 ( 1 )', '256.6 ( 5 )', '90.6 ( 42 )'], ['13', 'matti hautamäki', 'fin', '899.6', '241.2 ( 19 )', '233.1 ( 17 )', '209.6 ( 29 )', '215.7 ( 19 )'], ['14', 'tom hilde', 'nor', '878.3', '277.9 ( 4 )', '251.7 ( 8 )', '253.5 ( 6 )', '95.2 ( 34 )']] |
saulo roston | https://en.wikipedia.org/wiki/Saulo_Roston | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27614707-1.html.csv | majority | in the television show ídolos brazil , saulo roston was safe for most of the episodes . | {'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'safe', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'result', 'safe'], 'result': True, 'ind': 0, 'tointer': 'for the result records of all rows , most of them fuzzily match to safe .', 'tostr': 'most_eq { all_rows ; result ; safe } = true'} | most_eq { all_rows ; result ; safe } = true | for the result records of all rows , most of them fuzzily match to safe . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 'safe_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 'safe_4': 'safe'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 'safe_4': [0]} | ['week', 'theme', 'song choice', 'original artist', 'order', 'result'] | [['audition', "auditioner 's choice", 'bem que se quis', 'marisa monte', 'n / a', 'advanced'], ['theater', 'first solo', 'n / a', 'n / a', 'n / a', 'advanced'], ['top 24', 'top 12 men', 'como vai você', 'roberto carlos', '7', 'advanced'], ['top 12', 'sing your idol', 'beija eu', 'marisa monte', '4', 'safe'], ['top 11', '70s night', 'mania de você', 'rita lee', '10', 'safe'], ['top 10', 'the roguish', 'já tive mulheres', 'martinho da vila', '6', 'safe'], ['top 9', 'broken heart songs', 'tem que ser você', 'victor & léo', '4', 'bottom 3'], ['top 7', '80s night', 'você é linda', 'caetano veloso', '2', 'safe'], ['top 6', 'cult trash', 'aguenta coração', 'josé augusto', '1', 'safe'], ['top 5', 'kings of the pop', 'amor i love you', 'marisa monte', '4', 'safe'], ['top 5', 'kings of the pop', 'your song', 'elton john', '9', 'safe'], ['top 4', 'dedicate a song', 'monalisa', 'jorge vercilo', '1', 'safe'], ['top 4', 'my soundtrack', 'eu sei que vou te amar', 'tom jobim', '5', 'safe'], ['top 3', "judge 's choice", 'pro dia nascer feliz', 'cazuza', '1', 'safe'], ['top 3', "judge 's choice", 'fácil', 'jota quest', '4', 'safe'], ['top 3', "judge 's choice", 'o portão', 'roberto carlos', '7', 'safe'], ['top 2', "winner 's single 1", 'nova paixão', 'saulo roston', '1', 'winner'], ['top 2', 'best of the season', 'your song', 'elton john', '3', 'winner']] |
cale yarborough | https://en.wikipedia.org/wiki/Cale_Yarborough | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1145778-1.html.csv | unique | 1972 was the only year in which cale yarborough finished in 10th place . | {'scope': 'all', 'row': '4', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': '10', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'finish', '10'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose finish record is equal to 10 .', 'tostr': 'filter_eq { all_rows ; finish ; 10 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; finish ; 10 } }', 'tointer': 'select the rows whose finish record is equal to 10 . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'finish', '10'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose finish record is equal to 10 .', 'tostr': 'filter_eq { all_rows ; finish ; 10 }'}, 'year'], 'result': '1972', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; finish ; 10 } ; year }'}, '1972'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; finish ; 10 } ; year } ; 1972 }', 'tointer': 'the year record of this unqiue row is 1972 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; finish ; 10 } } ; eq { hop { filter_eq { all_rows ; finish ; 10 } ; year } ; 1972 } } = true', 'tointer': 'select the rows whose finish record is equal to 10 . there is only one such row in the table . the year record of this unqiue row is 1972 .'} | and { only { filter_eq { all_rows ; finish ; 10 } } ; eq { hop { filter_eq { all_rows ; finish ; 10 } ; year } ; 1972 } } = true | select the rows whose finish record is equal to 10 . there is only one such row in the table . the year record of this unqiue row is 1972 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'finish_7': 7, '10_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '1972_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'finish_7': 'finish', '10_8': '10', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '1972_10': '1972'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'finish_7': [0], '10_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '1972_10': [3]} | ['year', 'start', 'qual', 'rank', 'finish', 'laps'] | [['1966', '24', '159.794', '15', '28', '0'], ['1967', '20', '162.830', '30', '17', '176'], ['1971', '14', '170.770', '19', '16', '140'], ['1972', '32', '178.864', '33', '10', '193']] |
1965 - 66 boston celtics season | https://en.wikipedia.org/wiki/1965%E2%80%9366_Boston_Celtics_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17342287-8.html.csv | ordinal | the march 13 game against the baltimore bullets was the second highest score recorded by the boston celtics in the 1965 - 66 boston celtics season . | {'row': '8', 'col': '4', 'order': '2', 'col_other': '2,3', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'score', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; score ; 2 }'}, 'date'], 'result': 'march 13', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; score ; 2 } ; date }'}, 'march 13'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; score ; 2 } ; date } ; march 13 }', 'tointer': 'select the row whose score record of all rows is 2nd maximum . the date record of this row is march 13 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'score', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; score ; 2 }'}, 'opponent'], 'result': 'baltimore bullets', 'ind': 3, 'tostr': 'hop { nth_argmax { all_rows ; score ; 2 } ; opponent }'}, 'baltimore bullets'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { nth_argmax { all_rows ; score ; 2 } ; opponent } ; baltimore bullets }', 'tointer': 'the opponent record of this row is baltimore bullets .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { hop { nth_argmax { all_rows ; score ; 2 } ; date } ; march 13 } ; eq { hop { nth_argmax { all_rows ; score ; 2 } ; opponent } ; baltimore bullets } } = true', 'tointer': 'select the row whose score record of all rows is 2nd maximum . the date record of this row is march 13 . the opponent record of this row is baltimore bullets .'} | and { eq { hop { nth_argmax { all_rows ; score ; 2 } ; date } ; march 13 } ; eq { hop { nth_argmax { all_rows ; score ; 2 } ; opponent } ; baltimore bullets } } = true | select the row whose score record of all rows is 2nd maximum . the date record of this row is march 13 . the opponent record of this row is baltimore bullets . | 7 | 6 | {'and_5': 5, 'result_6': 6, 'str_eq_2': 2, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_7': 7, 'score_8': 8, '2_9': 9, 'date_10': 10, 'march 13_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'opponent_12': 12, 'baltimore bullets_13': 13} | {'and_5': 'and', 'result_6': 'true', 'str_eq_2': 'str_eq', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_7': 'all_rows', 'score_8': 'score', '2_9': '2', 'date_10': 'date', 'march 13_11': 'march 13', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'opponent_12': 'opponent', 'baltimore bullets_13': 'baltimore bullets'} | {'and_5': [6], 'result_6': [], 'str_eq_2': [5], 'str_hop_1': [2], 'nth_argmax_0': [1, 3], 'all_rows_7': [0], 'score_8': [0], '2_9': [0], 'date_10': [1], 'march 13_11': [2], 'str_eq_4': [5], 'str_hop_3': [4], 'opponent_12': [3], 'baltimore bullets_13': [4]} | ['game', 'date', 'opponent', 'score', 'location / attendance', 'record'] | [['70', 'march 1', 'st louis hawks', '120 - 95', 'kiel auditorium', '47 - 23'], ['71', 'march 2', 'new york knickerbockers', '140 - 104', 'boston garden', '48 - 23'], ['72', 'march 4', 'st louis hawks', '112 - 132', 'providence , ri', '48 - 24'], ['73', 'march 5', 'philadelphia 76ers', '85 - 102', 'convention hall', '48 - 25'], ['74', 'march 6', 'philadelphia 76ers', '110 - 113', 'boston garden', '48 - 26'], ['75', 'march 7', 'st louis hawks', '106 - 104', 'memphis , tn', '49 - 26'], ['76', 'march 10', 'cincinnati royals', '124 - 120', 'cincinnati gardens', '50 - 26'], ['77', 'march 13', 'baltimore bullets', '129 - 98', 'boston garden', '51 - 26'], ['78', 'march 17', 'detroit pistons', '128 - 103', 'cobo arena', '52 - 26'], ['79', 'march 19', 'new york knicks', '126 - 113', 'madison square garden', '53 - 26'], ['80', 'march 20', 'cincinnati royals', '121 - 104', 'boston garden', '54 - 26']] |
kristine kunce | https://en.wikipedia.org/wiki/Kristine_Kunce | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14803173-3.html.csv | unique | kristine kunce played on a grass surface on one occasion . | {'scope': 'all', 'row': '4', 'col': '3', 'col_other': 'n/a', 'criterion': 'equal', 'value': 'grass', 'subset': None} | {'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'surface', 'grass'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose surface record fuzzily matches to grass .', 'tostr': 'filter_eq { all_rows ; surface ; grass }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; surface ; grass } } = true', 'tointer': 'select the rows whose surface record fuzzily matches to grass . there is only one such row in the table .'} | only { filter_eq { all_rows ; surface ; grass } } = true | select the rows whose surface record fuzzily matches to grass . there is only one such row in the table . | 2 | 2 | {'only_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'surface_4': 4, 'grass_5': 5} | {'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'surface_4': 'surface', 'grass_5': 'grass'} | {'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'surface_4': [0], 'grass_5': [0]} | ['date', 'tournament', 'surface', 'partnering', 'opponents in the final', 'score'] | [['19 april 1993', 'kuala lumpar , malaysia', 'hard ( i )', 'nicole arendt', 'patty fendick meredith mcgrath', '6 - 4 , 7 - 6 ( 2 )'], ['4 october 1993', 'taiwan', 'hard', 'jo - anne faull', 'yayuk basuki nana miyagi', '6 - 4 , 6 - 2'], ['18 april 1994', 'kallang , singapore', 'hard', 'nicole arendt', 'patty fendick meredith mcgrath', '6 - 4 , 6 - 2'], ['12 june 1995', 'birmingham , england , united kingdom', 'grass', 'nicole bradtke', 'manon bollegraf rennae stubbs', '3 - 6 , 6 - 4 , 6 - 4'], ['1 january 1996', 'auckland , australia', 'hard', 'jill hetherington', 'els callens julie halard - decugis', '6 - 1 , 6 - 0'], ['23 february 1998', 'memphis , usa', 'hard', 'cătălina cristea', 'serena williams venus williams', '7 - 5 , 6 - 2'], ['11 september 1995', 'gold coast , australia', 'hard', 'irina spîrlea', 'corina morariu larisa neiland', '6 - 3 , 6 - 3']] |
1963 new york jets season | https://en.wikipedia.org/wiki/1963_New_York_Jets_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13983625-1.html.csv | aggregation | in the 1963 jets season , there were a total of 26748 people attendance for the games against the bills . | {'scope': 'subset', 'col': '6', 'type': 'sum', 'result': '26748', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'buffalo bills'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'buffalo bills'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; opponent ; buffalo bills }', 'tointer': 'select the rows whose opponent record fuzzily matches to buffalo bills .'}, 'attendance'], 'result': '26748', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; opponent ; buffalo bills } ; attendance }'}, '26748'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; opponent ; buffalo bills } ; attendance } ; 26748 } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to buffalo bills . the sum of the attendance record of these rows is 26748 .'} | round_eq { sum { filter_eq { all_rows ; opponent ; buffalo bills } ; attendance } ; 26748 } = true | select the rows whose opponent record fuzzily matches to buffalo bills . the sum of the attendance record of these rows is 26748 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'opponent_5': 5, 'buffalo bills_6': 6, 'attendance_7': 7, '26748_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'opponent_5': 'opponent', 'buffalo bills_6': 'buffalo bills', 'attendance_7': 'attendance', '26748_8': '26748'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'opponent_5': [0], 'buffalo bills_6': [0], 'attendance_7': [1], '26748_8': [2]} | ['week', 'date', 'opponent', 'result', 'game site', 'attendance'] | [['1', '1963 - 09 - 08', 'boston patriots', 'l 38 - 14', 'fenway park', '24120'], ['3', '1963 - 09 - 22', 'houston oilers', 'w 24 - 17', 'polo grounds', '9336'], ['4', '1963 - 09 - 28', 'oakland raiders', 'w 10 - 7', 'polo grounds', '17100'], ['5', '1963 - 10 - 05', 'boston patriots', 'w 31 - 24', 'polo grounds', '16769'], ['6', '1963 - 10 - 13', 'san diego chargers', 'l 24 - 20', 'balboa stadium', '27189'], ['7', '1963 - 10 - 20', 'oakland raiders', 'l 49 - 26', 'frank youell field', '15557'], ['8', '1963 - 10 - 26', 'denver broncos', 't 35 - 35', 'polo grounds', '22553'], ['9', '1963 - 11 - 02', 'san diego chargers', 'l 53 - 7', 'polo grounds', '20798'], ['10', '1963 - 11 - 10', 'houston oilers', 'l 31 - 27', 'jeppesen stadium', '23619'], ['11', '1963 - 11 - 17', 'denver broncos', 'w 14 - 9', 'bears stadium', '14247'], ['12', '1963 - 12 - 01', 'kansas city chiefs', 'w 17 - 0', 'polo grounds', '18824'], ['13', '1963 - 12 - 08', 'buffalo bills', 'l 45 - 14', 'war memorial stadium', '20222'], ['14', '1963 - 12 - 14', 'buffalo bills', 'l 19 - 10', 'polo grounds', '6526'], ['15', '1963 - 12 - 22', 'kansas city chiefs', 'l 48 - 0', 'municipal stadium', '12202']] |
maurício gugelmin | https://en.wikipedia.org/wiki/Maur%C3%ADcio_Gugelmin | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1226502-2.html.csv | superlative | the highest number of points was received by leyton house march racing team . | {'scope': 'all', 'col_superlative': '5', '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', 'pts'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; pts }'}, 'entrant'], 'result': 'leyton house march racing team', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; pts } ; entrant }'}, 'leyton house march racing team'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; pts } ; entrant } ; leyton house march racing team } = true', 'tointer': 'select the row whose pts record of all rows is maximum . the entrant record of this row is leyton house march racing team .'} | eq { hop { argmax { all_rows ; pts } ; entrant } ; leyton house march racing team } = true | select the row whose pts record of all rows is maximum . the entrant record of this row is leyton house march racing team . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'pts_5': 5, 'entrant_6': 6, 'leyton house march racing team_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'pts_5': 'pts', 'entrant_6': 'entrant', 'leyton house march racing team_7': 'leyton house march racing team'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'pts_5': [0], 'entrant_6': [1], 'leyton house march racing team_7': [2]} | ['year', 'entrant', 'chassis', 'engine', 'pts'] | [['1988', 'leyton house march racing team', 'march 881', 'judd v8', '5'], ['1989', 'leyton house racing', 'march 881', 'judd v8', '4'], ['1989', 'leyton house racing', 'march cg891', 'judd v8', '4'], ['1990', 'leyton house', 'leyton house cg901', 'judd v8', '1'], ['1991', 'leyton house', 'leyton house cg911', 'ilmor v10', '0'], ['1992', 'sasol jordan yamaha', 'jordan 192', 'yamaha v12', '0']] |
nfl europe | https://en.wikipedia.org/wiki/NFL_Europe | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-160994-4.html.csv | superlative | the oldest stadium used by nfl europe opened in 1877 . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '13', 'value_mentioned': 'yes', 'max_or_min': 'min', 'other_col': '2', 'subset': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'min', 'args': ['all_rows', 'opened'], 'result': '1877', 'ind': 0, 'tostr': 'min { all_rows ; opened }', 'tointer': 'the minimum opened record of all rows is 1877 .'}, '1877'], 'result': True, 'ind': 1, 'tostr': 'eq { min { all_rows ; opened } ; 1877 }', 'tointer': 'the minimum opened record of all rows is 1877 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'opened'], 'result': None, 'ind': 2, 'tostr': 'argmin { all_rows ; opened }'}, 'stadium'], 'result': 'stamford bridge', 'ind': 3, 'tostr': 'hop { argmin { all_rows ; opened } ; stadium }'}, 'stamford bridge'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { argmin { all_rows ; opened } ; stadium } ; stamford bridge }', 'tointer': 'the stadium record of the row with superlative opened record is stamford bridge .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { min { all_rows ; opened } ; 1877 } ; eq { hop { argmin { all_rows ; opened } ; stadium } ; stamford bridge } } = true', 'tointer': 'the minimum opened record of all rows is 1877 . the stadium record of the row with superlative opened record is stamford bridge .'} | and { eq { min { all_rows ; opened } ; 1877 } ; eq { hop { argmin { all_rows ; opened } ; stadium } ; stamford bridge } } = true | the minimum opened record of all rows is 1877 . the stadium record of the row with superlative opened record is stamford bridge . | 6 | 6 | {'and_5': 5, 'result_6': 6, 'eq_1': 1, 'min_0': 0, 'all_rows_7': 7, 'opened_8': 8, '1877_9': 9, 'str_eq_4': 4, 'str_hop_3': 3, 'argmin_2': 2, 'all_rows_10': 10, 'opened_11': 11, 'stadium_12': 12, 'stamford bridge_13': 13} | {'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'min_0': 'min', 'all_rows_7': 'all_rows', 'opened_8': 'opened', '1877_9': '1877', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'argmin_2': 'argmin', 'all_rows_10': 'all_rows', 'opened_11': 'opened', 'stadium_12': 'stadium', 'stamford bridge_13': 'stamford bridge'} | {'and_5': [6], 'result_6': [], 'eq_1': [5], 'min_0': [1], 'all_rows_7': [0], 'opened_8': [0], '1877_9': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'argmin_2': [3], 'all_rows_10': [2], 'opened_11': [2], 'stadium_12': [3], 'stamford bridge_13': [4]} | ['team', 'stadium', 'capacity', 'opened', 'city'] | [['amsterdam admirals', 'amsterdam arena', '51859', '1996', 'amsterdam , the netherlands'], ['amsterdam admirals', 'olympisch stadion', '31600', '1928', 'amsterdam , the netherlands'], ['barcelona dragons', 'mini estadi', '15276', '1982', 'barcelona , spain'], ['barcelona dragons', 'estadi olímpic lluís companys', '56000', '1929', 'barcelona , spain'], ['berlin thunder', 'olympiastadion', '76000', '1936', 'berlin , germany'], ['berlin thunder', 'f l jahn sportpark', '19500', '1951', 'berlin , germany'], ['cologne centurions', 'rheinenergiestadion', '50374', '1923', 'cologne , germany'], ['frankfurt galaxy', 'commerzbank - arena waldstadion ( 1925 - 2005 )', '52000', '1925', 'frankfurt , germany'], ['hamburg sea devils', 'aol arena', '55989', '2000', 'hamburg , germany'], ['london / england monarchs', 'ashton gate', '21500', '1900', 'bristol , england'], ['london / england monarchs', 'alexander stadium', '7600', '1976', 'birmingham , england'], ['london / england monarchs', 'crystal palace national sports centre', '15500', '1964', 'london , england'], ['london / england monarchs', 'stamford bridge', '42449', '1877', 'london , england'], ['london / england monarchs', 'white hart lane', '36240', '1899', 'london , england'], ['london / england monarchs', 'wembley stadium', '80000', '1923', 'london , england'], ['rhein fire', 'ltu arena', '51500', '2004', 'düsseldorf , germany'], ['rhein fire', 'arena aufschalke', '61524', '2001', 'gelsenkirchen , germany'], ['rhein fire', 'rheinstadion', '55900', '1926', 'düsseldorf , germany'], ['scottish claymores', 'hampden park', '52500', '1903', 'glasgow , scotland'], ['scottish claymores', 'murrayfield stadium', '67500', '1925', 'edinburgh , scotland']] |
2008 - 09 detroit red wings season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Detroit_Red_Wings_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17371135-3.html.csv | count | there were two games in this time span where one of the teams went scoreless . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': '0', 'result': '2', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'score', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose score record fuzzily matches to 0 .', 'tostr': 'filter_eq { all_rows ; score ; 0 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; score ; 0 } }', 'tointer': 'select the rows whose score record fuzzily matches to 0 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; score ; 0 } } ; 2 } = true', 'tointer': 'select the rows whose score record fuzzily matches to 0 . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; score ; 0 } } ; 2 } = true | select the rows whose score record fuzzily matches to 0 . 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, 'score_5': 5, '0_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', 'score_5': 'score', '0_6': '0', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'score_5': [0], '0_6': [0], '2_7': [2]} | ['date', 'visitor', 'score', 'home', 'decision', 'record'] | [['september 24', 'montreal', '3 - 2', 'detroit', 'howard', '0 - 0 - 1'], ['september 25', 'detroit', '4 - 3', 'boston', 'larsson', '1 - 0 - 1'], ['september 26', 'boston', '2 - 1', 'detroit', 'conklin', '1 - 1 - 1'], ['september 28', 'atlanta', '0 - 4', 'detroit', 'osgood', '2 - 1 - 1'], ['september 30', 'detroit', '1 - 2', 'montreal', 'howard', '2 - 1 - 2'], ['october 1', 'detroit', '4 - 1', 'atlanta', 'osgood', '3 - 1 - 2'], ['october 3', 'toronto', '3 - 5', 'detroit', 'conklin', '4 - 1 - 2'], ['october 4', 'detroit', '4 - 3', 'toronto', 'osgood', '5 - 1 - 2'], ['october 5', 'buffalo', '0 - 3', 'detroit', 'conklin', '6 - 1 - 2']] |
1987 in film | https://en.wikipedia.org/wiki/1987_in_film | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-171293-2.html.csv | comparative | dirty dancing had a higher gross than good morning , vietnam . | {'row_1': '3', 'row_2': '6', 'col': '5', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'dirty dancing'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose title record fuzzily matches to dirty dancing .', 'tostr': 'filter_eq { all_rows ; title ; dirty dancing }'}, 'gross'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; title ; dirty dancing } ; gross }', 'tointer': 'select the rows whose title record fuzzily matches to dirty dancing . take the gross record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'good morning , vietnam'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose title record fuzzily matches to good morning , vietnam .', 'tostr': 'filter_eq { all_rows ; title ; good morning , vietnam }'}, 'gross'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; title ; good morning , vietnam } ; gross }', 'tointer': 'select the rows whose title record fuzzily matches to good morning , vietnam . take the gross record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; title ; dirty dancing } ; gross } ; hop { filter_eq { all_rows ; title ; good morning , vietnam } ; gross } } = true', 'tointer': 'select the rows whose title record fuzzily matches to dirty dancing . take the gross record of this row . select the rows whose title record fuzzily matches to good morning , vietnam . take the gross record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; title ; dirty dancing } ; gross } ; hop { filter_eq { all_rows ; title ; good morning , vietnam } ; gross } } = true | select the rows whose title record fuzzily matches to dirty dancing . take the gross record of this row . select the rows whose title record fuzzily matches to good morning , vietnam . take the gross 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, 'title_7': 7, 'dirty dancing_8': 8, 'gross_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'title_11': 11, 'good morning , vietnam_12': 12, 'gross_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', 'title_7': 'title', 'dirty dancing_8': 'dirty dancing', 'gross_9': 'gross', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'title_11': 'title', 'good morning , vietnam_12': 'good morning , vietnam', 'gross_13': 'gross'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'title_7': [0], 'dirty dancing_8': [0], 'gross_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'title_11': [1], 'good morning , vietnam_12': [1], 'gross_13': [3]} | ['rank', 'title', 'studio', 'director', 'gross'] | [['1', 'fatal attraction', 'paramount', 'adrian lyne', '320145693'], ['2', 'beverly hills cop ii', 'paramount', 'tony scott', '299965036'], ['3', 'dirty dancing', 'vestron', 'emile ardolino', '213954274'], ['4', 'the living daylights', 'united artists', 'john glen', '191200000'], ['5', 'three men and a baby', 'touchstone', 'leonard nimoy', '167780960'], ['6', 'good morning , vietnam', 'touchstone', 'barry levinson', '123922370'], ['7', 'lethal weapon', 'warner bros', 'richard donner', '120207127'], ['8', 'the secret of my success', 'universal', 'herbert ross', '110996879'], ['9', 'predator', 'fox', 'john mctiernan', '98267558'], ['10', 'moonstruck', 'mgm', 'norman jewison', '80640528'], ['11', 'the untouchables', 'paramount', 'brian de palma', '76270454'], ['12', 'broadcast news', 'fox', 'james l brooks', '67331309'], ['13', 'dragnet', 'universal', 'tom mankiewicz', '66673516'], ['14', 'outrageous fortune', 'touchstone', 'arthur hiller', '65864741'], ['15', 'stakeout', 'touchstone', 'john badham', '65673233'], ['16', 'the witches of eastwick', 'warner bros', 'george miller', '63766510'], ['17', 'throw momma from the train', 'orion', 'danny devito', '57915972'], ['18', 'la bamba', 'columbia', 'luis valdez', '54215416'], ['19', 'robocop', 'orion', 'paul verhoeven', '53424681'], ['20', 'eddie murphy raw', 'paramount', 'robert townsend', '50505655']] |
moon landing | https://en.wikipedia.org/wiki/Moon_landing | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1558077-2.html.csv | aggregation | the average mass in kilograms of the 7 combined ranger us missions was 340 . | {'scope': 'subset', 'col': '2', 'type': 'average', 'result': '340', 'subset': {'col': '1', 'criterion': 'equal', 'value': 'ranger'}} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'us mission', 'ranger'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; us mission ; ranger }', 'tointer': 'select the rows whose us mission record fuzzily matches to ranger .'}, 'mass ( kg )'], 'result': '340', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; us mission ; ranger } ; mass ( kg ) }'}, '340'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; us mission ; ranger } ; mass ( kg ) } ; 340 } = true', 'tointer': 'select the rows whose us mission record fuzzily matches to ranger . the average of the mass ( kg ) record of these rows is 340 .'} | round_eq { avg { filter_eq { all_rows ; us mission ; ranger } ; mass ( kg ) } ; 340 } = true | select the rows whose us mission record fuzzily matches to ranger . the average of the mass ( kg ) record of these rows is 340 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'us mission_5': 5, 'ranger_6': 6, 'mass (kg)_7': 7, '340_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'us mission_5': 'us mission', 'ranger_6': 'ranger', 'mass (kg)_7': 'mass ( kg )', '340_8': '340'} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'us mission_5': [0], 'ranger_6': [0], 'mass (kg)_7': [1], '340_8': [2]} | ['us mission', 'mass ( kg )', 'launch vehicle', 'launched', 'mission goal', 'mission result'] | [['pioneer 0', '38', 'thor - able', '17 august 1958', 'lunar orbit', 'failure - first stage explosion , destroyed'], ['pioneer 1', '34', 'thor - able', '11 october 1958', 'lunar orbit', 'failure - software error , reentry'], ['pioneer 2', '39', 'thor - able', '8 november 1958', 'lunar orbit', 'failure - third stage misfire , reentry'], ['pioneer 3', '6', 'juno', '6 december 1958', 'lunar flyby', 'failure - first stage misfire , reentry'], ['pioneer p - 1', '168', 'atlas - able', '24 september 1959', 'lunar orbit', 'failure - pad explosion , destroyed'], ['pioneer p - 3', '168', 'atlas - able', '29 november 1959', 'lunar orbit', 'failure - payload shroud , destroyed'], ['pioneer p - 30', '175', 'atlas - able', '25 september 1960', 'lunar orbit', 'failure - second stage anomaly , reentry'], ['pioneer p - 31', '175', 'atlas - able', '15 december 1960', 'lunar orbit', 'failure - first stage explosion , destroyed'], ['ranger 1', '306', 'atlas - agena', '23 august 1961', 'prototype test', 'failure - upper stage anomaly , reentry'], ['ranger 2', '304', 'atlas - agena', '18 november 1961', 'prototype test', 'failure - upper stage anomaly , reentry'], ['ranger 3', '330', 'atlas - agena', '26 january 1962', 'moon landing', 'failure - booster guidance , solar orbit'], ['ranger 5', '342', 'atlas - agena', '18 october 1962', 'moon landing', 'failure - spacecraft power , solar orbit'], ['ranger 6', '367', 'atlas - agena', '30 january 1964', 'lunar impact', 'failure - spacecraft camera , crash impact'], ['ranger 7', '367', 'atlas - agena', '28 july 1964', 'lunar impact', 'success - returned 4308 photos , crash impact'], ['ranger 8', '367', 'atlas - agena', '17 february 1965', 'lunar impact', 'success - returned 7137 photos , crash impact']] |
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 | count | three channels have alan massengale as the studio host for the los angeles lakers . | {'scope': 'all', 'criterion': 'equal', 'value': 'alan massengale', 'result': '3', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'studio host', 'alan massengale'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose studio host record fuzzily matches to alan massengale .', 'tostr': 'filter_eq { all_rows ; studio host ; alan massengale }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; studio host ; alan massengale } }', 'tointer': 'select the rows whose studio host record fuzzily matches to alan massengale . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; studio host ; alan massengale } } ; 3 } = true', 'tointer': 'select the rows whose studio host record fuzzily matches to alan massengale . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; studio host ; alan massengale } } ; 3 } = true | select the rows whose studio host record fuzzily matches to alan massengale . 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, 'studio host_5': 5, 'alan massengale_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', 'studio host_5': 'studio host', 'alan massengale_6': 'alan massengale', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'studio host_5': [0], 'alan massengale_6': [0], '3_7': [2]} | ['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']] |
miss usa 1980 | https://en.wikipedia.org/wiki/Miss_USA_1980 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15532342-2.html.csv | majority | the majority of miss usa 1980 contestants scored under 9 in the preliminary average . | {'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '9.0', 'subset': None} | {'func': 'most_less', 'args': ['all_rows', 'preliminary average', '9.0'], 'result': True, 'ind': 0, 'tointer': 'for the preliminary average records of all rows , most of them are less than 9.0 .', 'tostr': 'most_less { all_rows ; preliminary average ; 9.0 } = true'} | most_less { all_rows ; preliminary average ; 9.0 } = true | for the preliminary average records of all rows , most of them are less than 9.0 . | 1 | 1 | {'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'preliminary average_3': 3, '9.0_4': 4} | {'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'preliminary average_3': 'preliminary average', '9.0_4': '9.0'} | {'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'preliminary average_3': [0], '9.0_4': [0]} | ['state', 'preliminary average', 'interview', 'swimsuit', 'evening gown', 'semifinal average'] | [['nebraska', '8.450 ( 5 )', '7.938 ( 10 )', '7.489 ( 11 )', '7.832 ( 8 )', '7.753 ( 10 )'], ['arizona', '8.317 ( 8 )', '8.950 ( 4 )', '8.670 ( 4 )', '8.701 ( 2 )', '8.774 ( 2 )'], ['south carolina', '9.086 ( 1 )', '9.082 ( 1 )', '9.097 ( 1 )', '9.567 ( 1 )', '9.249 ( 1 )'], ['minnesota', '8.083 ( 12 )', '7.858 ( 11 )', '7.031 ( 12 )', '7.518 ( 12 )', '7.469 ( 12 )'], ['texas', '8.503 ( 4 )', '7.417 ( 12 )', '8.008 ( 9 )', '7.703 ( 10 )', '7.709 ( 11 )'], ['florida', '8.924 ( 3 )', '9.029 ( 2 )', '8.953 ( 2 )', '8.313 ( 4 )', '8.765 ( 3 )'], ['alabama', '8.334 ( 7 )', '8.822 ( 5 )', '8.775 ( 3 )', '8.268 ( 5 )', '8.621 ( 4 )'], ['new mexico', '8.998 ( 2 )', '8.804 ( 6 )', '8.438 ( 5 )', '7.968 ( 7 )', '8.403 ( 6 )'], ['maryland', '8.344 ( 6 )', '8.498 ( 7 )', '8.167 ( 8 )', '8.176 ( 6 )', '8.280 ( 7 )'], ['new hampshire', '8.104 ( 11 )', '8.029 ( 9 )', '7.826 ( 10 )', '7.625 ( 11 )', '7.827 ( 9 )'], ['kentucky', '8.247 ( 9 )', '8.989 ( 3 )', '8.347 ( 6 )', '8.457 ( 3 )', '8.598 ( 5 )']] |
2003 belarusian premier league | https://en.wikipedia.org/wiki/2003_Belarusian_Premier_League | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14748588-1.html.csv | aggregation | the average capacity of the venues in the belarusian premier league is 9037 . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '9037', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'capacity'], 'result': '9037', 'ind': 0, 'tostr': 'avg { all_rows ; capacity }'}, '9037'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; capacity } ; 9037 } = true', 'tointer': 'the average of the capacity record of all rows is 9037 .'} | round_eq { avg { all_rows ; capacity } ; 9037 } = true | the average of the capacity record of all rows is 9037 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'capacity_4': 4, '9037_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'capacity_4': 'capacity', '9037_5': '9037'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'capacity_4': [0], '9037_5': [1]} | ['team', 'location', 'venue', 'capacity', 'position in 2002'] | [['bate', 'borisov', 'city stadium , borisov', '5500', '1'], ['neman', 'grodno', 'neman', '6300', '2'], ['shakhtyor', 'soligorsk', 'stroitel', '5000', '3'], ['torpedo - ska', 'minsk', 'torpedo , minsk', '5200', '4'], ['torpedo', 'zhodino', 'torpedo , zhodino', '3020', '5'], ['gomel', 'gomel', 'central', '11800', '6'], ['dinamo minsk', 'minsk', 'dinamo , minsk', '41040', '7'], ['belshina', 'bobruisk', 'spartak , bobruisk', '3550', '8'], ['dnepr - transmash', 'mogilev', 'spartak , mogilev', '11200', '9'], ['dinamo brest', 'brest', 'osk brestskiy', '10080', '10'], ['slavia', 'mozyr', 'yunost', '5500', '11'], ['zvezda - va - bgu', 'minsk', 'traktor', '17600', '12'], ['molodechno - 2000', 'molodechno', 'city stadium , molodechno', '5500', '13'], ['darida', 'minsk raion', 'darida', '6000', 'first league , 1'], ['naftan', 'novopolotsk', 'atlant', '6500', 'first league , 2'], ['lokomotiv', 'minsk', 'lokomotiv', '800', 'first league , 3']] |
2008 indian premier league | https://en.wikipedia.org/wiki/2008_Indian_Premier_League | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15734036-10.html.csv | superlative | sanath jayasuriya was the 2008 indian premier league player who recorded the highest number of balls . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '3', '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', 'balls'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; balls }'}, 'player'], 'result': 'sanath jayasuriya', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; balls } ; player }'}, 'sanath jayasuriya'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; balls } ; player } ; sanath jayasuriya } = true', 'tointer': 'select the row whose balls record of all rows is maximum . the player record of this row is sanath jayasuriya .'} | eq { hop { argmax { all_rows ; balls } ; player } ; sanath jayasuriya } = true | select the row whose balls record of all rows is maximum . the player record of this row is sanath jayasuriya . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'balls_5': 5, 'player_6': 6, 'sanath jayasuriya_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'balls_5': 'balls', 'player_6': 'player', 'sanath jayasuriya_7': 'sanath jayasuriya'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'balls_5': [0], 'player_6': [1], 'sanath jayasuriya_7': [2]} | ['player', 'team', 'inns', 'runs', 'balls'] | [['virender sehwag', 'delhi daredevils', '14', '406', '220'], ['yusuf pathan', 'rajasthan royals', '15', '435', '243'], ['sanath jayasuriya', 'mumbai indians', '14', '514', '309'], ['yuvraj singh', 'kings xi punjab', '14', '299', '184'], ['kumar sangakkara', 'kings xi punjab', '9', '320', '198']] |
2007 - 08 portland trail blazers season | https://en.wikipedia.org/wiki/2007%E2%80%9308_Portland_Trail_Blazers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11964047-10.html.csv | superlative | during this period of the 2007-08 portland trail blazers season , the portland trailblazers experienced their highest attendance on april 8th in their game against the los angeles lakers . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '4', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1,2,4', 'subset': None} | {'func': 'and', 'args': [{'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': 'april 8', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; attendance } ; date }'}, 'april 8'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; date } ; april 8 }', 'tointer': 'select the row whose attendance record of all rows is maximum . the date record of this row is april 8 .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'attendance'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; attendance }'}, 'visitor'], 'result': 'los angeles lakers', 'ind': 3, 'tostr': 'hop { argmax { all_rows ; attendance } ; visitor }'}, 'los angeles lakers'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; visitor } ; los angeles lakers }', 'tointer': 'the visitor record of this row is los angeles lakers .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'attendance'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; attendance }'}, 'home'], 'result': 'portland trail blazers', 'ind': 5, 'tostr': 'hop { argmax { all_rows ; attendance } ; home }'}, 'portland trail blazers'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; home } ; portland trail blazers }', 'tointer': 'the home record of this row is portland trail blazers .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { argmax { all_rows ; attendance } ; visitor } ; los angeles lakers } ; eq { hop { argmax { all_rows ; attendance } ; home } ; portland trail blazers } }', 'tointer': 'the visitor record of this row is los angeles lakers . the home record of this row is portland trail blazers .'}], 'result': True, 'ind': 8, 'tostr': 'and { eq { hop { argmax { all_rows ; attendance } ; date } ; april 8 } ; and { eq { hop { argmax { all_rows ; attendance } ; visitor } ; los angeles lakers } ; eq { hop { argmax { all_rows ; attendance } ; home } ; portland trail blazers } } } = true', 'tointer': 'select the row whose attendance record of all rows is maximum . the date record of this row is april 8 . the visitor record of this row is los angeles lakers . the home record of this row is portland trail blazers .'} | and { eq { hop { argmax { all_rows ; attendance } ; date } ; april 8 } ; and { eq { hop { argmax { all_rows ; attendance } ; visitor } ; los angeles lakers } ; eq { hop { argmax { all_rows ; attendance } ; home } ; portland trail blazers } } } = true | select the row whose attendance record of all rows is maximum . the date record of this row is april 8 . the visitor record of this row is los angeles lakers . the home record of this row is portland trail blazers . | 11 | 9 | {'and_8': 8, 'result_9': 9, 'str_eq_2': 2, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_10': 10, 'attendance_11': 11, 'date_12': 12, 'april 8_13': 13, 'and_7': 7, 'str_eq_4': 4, 'str_hop_3': 3, 'visitor_14': 14, 'los angeles lakers_15': 15, 'str_eq_6': 6, 'str_hop_5': 5, 'home_16': 16, 'portland trail blazers_17': 17} | {'and_8': 'and', 'result_9': 'true', 'str_eq_2': 'str_eq', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_10': 'all_rows', 'attendance_11': 'attendance', 'date_12': 'date', 'april 8_13': 'april 8', 'and_7': 'and', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'visitor_14': 'visitor', 'los angeles lakers_15': 'los angeles lakers', 'str_eq_6': 'str_eq', 'str_hop_5': 'str_hop', 'home_16': 'home', 'portland trail blazers_17': 'portland trail blazers'} | {'and_8': [9], 'result_9': [], 'str_eq_2': [8], 'str_hop_1': [2], 'argmax_0': [1, 3, 5], 'all_rows_10': [0], 'attendance_11': [0], 'date_12': [1], 'april 8_13': [2], 'and_7': [8], 'str_eq_4': [7], 'str_hop_3': [4], 'visitor_14': [3], 'los angeles lakers_15': [4], 'str_eq_6': [7], 'str_hop_5': [6], 'home_16': [5], 'portland trail blazers_17': [6]} | ['date', 'visitor', 'score', 'home', 'leading scorer', 'attendance', 'record', 'streak'] | [['april 2', 'portland trail blazers', 'l 91 - 104', 'los angeles lakers', 'bryant : 36', 'staples center 18997', '38 - 37', 'l3'], ['april 3', 'houston rockets', 'l 95 - 86', 'portland trail blazers', 'mcgrady : 35', 'rose garden 19980', '38 - 38', 'l4'], ['april 6', 'san antonio spurs', 'l 72 - 65', 'portland trail blazers', 'duncan : 27', 'rose garden 19980', '38 - 39', 'l5'], ['april 8', 'los angeles lakers', 'w 103 - 112', 'portland trail blazers', 'bryant : 34', 'rose garden 20435', '39 - 39', 'w1'], ['april 11', 'portland trail blazers', 'l 86 - 103', 'sacramento kings', 'aldridge : 24', 'arco arena 13327', '39 - 40', 'l1'], ['april 12', 'dallas mavericks', 'w 105 - 108', 'portland trail blazers', 'nowitzki : 28', 'rose garden 19980', '40 - 40', 'w1'], ['april 15', 'memphis grizzlies', 'w 91 - 113', 'portland trail blazers', 'jones : 20', 'rose garden 19980', '41 - 40', 'w2'], ['april 16', 'portland trail blazers', 'l 91 - 100', 'phoenix suns', 'outlaw : 24', 'us airways center 18422', '41 - 41', 'l1']] |
1962 - 63 segunda división | https://en.wikipedia.org/wiki/1962%E2%80%9363_Segunda_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17724929-2.html.csv | superlative | the club real sociedad had the greatest positive goal difference of +33 in 1962 - 63 segunda división . | {'scope': 'all', 'col_superlative': '10', 'row_superlative': '4', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'goal difference'], 'result': '+ 33', 'ind': 0, 'tostr': 'max { all_rows ; goal difference }', 'tointer': 'the maximum goal difference record of all rows is + 33 .'}, '+ 33'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; goal difference } ; + 33 }', 'tointer': 'the maximum goal difference record of all rows is + 33 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'goal difference'], 'result': None, 'ind': 2, 'tostr': 'argmax { all_rows ; goal difference }'}, 'club'], 'result': 'real sociedad', 'ind': 3, 'tostr': 'hop { argmax { all_rows ; goal difference } ; club }'}, 'real sociedad'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { argmax { all_rows ; goal difference } ; club } ; real sociedad }', 'tointer': 'the club record of the row with superlative goal difference record is real sociedad .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { max { all_rows ; goal difference } ; + 33 } ; eq { hop { argmax { all_rows ; goal difference } ; club } ; real sociedad } } = true', 'tointer': 'the maximum goal difference record of all rows is + 33 . the club record of the row with superlative goal difference record is real sociedad .'} | and { eq { max { all_rows ; goal difference } ; + 33 } ; eq { hop { argmax { all_rows ; goal difference } ; club } ; real sociedad } } = true | the maximum goal difference record of all rows is + 33 . the club record of the row with superlative goal difference record is real sociedad . | 6 | 6 | {'and_5': 5, 'result_6': 6, 'eq_1': 1, 'max_0': 0, 'all_rows_7': 7, 'goal difference_8': 8, '+ 33_9': 9, 'str_eq_4': 4, 'str_hop_3': 3, 'argmax_2': 2, 'all_rows_10': 10, 'goal difference_11': 11, 'club_12': 12, 'real sociedad_13': 13} | {'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'max_0': 'max', 'all_rows_7': 'all_rows', 'goal difference_8': 'goal difference', '+ 33_9': '+ 33', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'argmax_2': 'argmax', 'all_rows_10': 'all_rows', 'goal difference_11': 'goal difference', 'club_12': 'club', 'real sociedad_13': 'real sociedad'} | {'and_5': [6], 'result_6': [], 'eq_1': [5], 'max_0': [1], 'all_rows_7': [0], 'goal difference_8': [0], '+ 33_9': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'argmax_2': [3], 'all_rows_10': [2], 'goal difference_11': [2], 'club_12': [3], 'real sociedad_13': [4]} | ['position', 'club', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference'] | [['1', 'pontevedra cf', '30', '41', '16', '9', '5', '44', '31', '+ 13'], ['2', 'rcd español', '30', '39', '17', '5', '8', '40', '24', '+ 16'], ['3', 'real santander', '30', '37', '15', '7', '8', '53', '39', '+ 14'], ['4', 'real sociedad', '30', '35', '14', '7', '9', '77', '44', '+ 33'], ['5', 'real gijón', '30', '34', '16', '2', '12', '50', '46', '+ 4'], ['6', 'rc celta de vigo', '30', '32', '13', '6', '11', '47', '31', '+ 16'], ['7', 'cd orense', '30', '31', '14', '3', '13', '43', '37', '+ 6'], ['8', 'deportivo alavés', '30', '30', '12', '6', '12', '43', '46', '- 3'], ['9', 'sd indauchu', '30', '30', '11', '8', '11', '46', '42', '+ 4'], ['10', 'burgos cf', '30', '29', '12', '5', '13', '39', '47', '- 8'], ['11', 'ud salamanca', '30', '27', '10', '7', '13', '40', '46', '- 6'], ['12', 'cd constancia', '30', '26', '11', '4', '15', '42', '51', '- 9'], ['13', 'up langreo', '30', '25', '8', '9', '13', '33', '42', '- 9'], ['14', 'atlético baleares', '30', '23', '9', '5', '16', '37', '51', '- 14'], ['15', 'cd basconia', '30', '21', '9', '3', '18', '31', '65', '- 34'], ['16', 'cd sabadell cf', '30', '20', '8', '4', '18', '43', '66', '- 23']] |
chinese units of measurement | https://en.wikipedia.org/wiki/Chinese_units_of_measurement | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-147235-16.html.csv | comparative | the loeng2 chinese unit of measurement has a lower imperial value than the daam3 chinese unit of measurement . | {'row_1': '4', 'row_2': '6', 'col': '6', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'jyutping', 'loeng2'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose jyutping record fuzzily matches to loeng2 .', 'tostr': 'filter_eq { all_rows ; jyutping ; loeng2 }'}, 'imperial value'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; jyutping ; loeng2 } ; imperial value }', 'tointer': 'select the rows whose jyutping record fuzzily matches to loeng2 . take the imperial value record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'jyutping', 'daam3'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose jyutping record fuzzily matches to daam3 .', 'tostr': 'filter_eq { all_rows ; jyutping ; daam3 }'}, 'imperial value'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; jyutping ; daam3 } ; imperial value }', 'tointer': 'select the rows whose jyutping record fuzzily matches to daam3 . take the imperial value record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; jyutping ; loeng2 } ; imperial value } ; hop { filter_eq { all_rows ; jyutping ; daam3 } ; imperial value } } = true', 'tointer': 'select the rows whose jyutping record fuzzily matches to loeng2 . take the imperial value record of this row . select the rows whose jyutping record fuzzily matches to daam3 . take the imperial value record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; jyutping ; loeng2 } ; imperial value } ; hop { filter_eq { all_rows ; jyutping ; daam3 } ; imperial value } } = true | select the rows whose jyutping record fuzzily matches to loeng2 . take the imperial value record of this row . select the rows whose jyutping record fuzzily matches to daam3 . take the imperial value 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, 'jyutping_7': 7, 'loeng2_8': 8, 'imperial value_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'jyutping_11': 11, 'daam3_12': 12, 'imperial value_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', 'jyutping_7': 'jyutping', 'loeng2_8': 'loeng2', 'imperial value_9': 'imperial value', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'jyutping_11': 'jyutping', 'daam3_12': 'daam3', 'imperial value_13': 'imperial value'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'jyutping_7': [0], 'loeng2_8': [0], 'imperial value_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'jyutping_11': [1], 'daam3_12': [1], 'imperial value_13': [3]} | ['jyutping', 'character', 'portuguese', 'relative value', 'metric value', 'imperial value'] | [['lei4', '厘', 'liz', '1 / 1600', '37.79931 mg', '~ 0.2133 dr'], ['fan1', '分', 'condorim', '1 / 1600', '377.9936375 mg', '~ 0.2133 dr'], ['cin4', '錢', 'maz', '1 / 160', '3.779936375 g', '~ 2.1333 dr'], ['loeng2', '兩', 'tael', '1 / 16', '37.79936375 g', '~ 1.3333 oz'], ['gan1', '斤', 'cate', '1', '604.78982 g', '~ 1.3333 lb'], ['daam3', '担 / 擔', 'pico', '100', '60.478982 kg', '~ 133.3333 lb']] |
colts - patriots rivalry | https://en.wikipedia.org/wiki/Colts%E2%80%93Patriots_rivalry | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13342861-3.html.csv | aggregation | in 1978 the new england patriots scored a total of 62 points against the baltimore colts . | {'scope': 'subset', 'col': '4', 'type': 'sum', 'result': '62', 'subset': {'col': '1', 'criterion': 'equal', 'value': '1978'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'year', '1978'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; year ; 1978 }', 'tointer': 'select the rows whose year record is equal to 1978 .'}, 'result'], 'result': '62', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; year ; 1978 } ; result }'}, '62'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; year ; 1978 } ; result } ; 62 } = true', 'tointer': 'select the rows whose year record is equal to 1978 . the sum of the result record of these rows is 62 .'} | round_eq { sum { filter_eq { all_rows ; year ; 1978 } ; result } ; 62 } = true | select the rows whose year record is equal to 1978 . the sum of the result record of these rows is 62 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'year_5': 5, '1978_6': 6, 'result_7': 7, '62_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'year_5': 'year', '1978_6': '1978', 'result_7': 'result', '62_8': '62'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'year_5': [0], '1978_6': [0], 'result_7': [1], '62_8': [2]} | ['year', 'date', 'winner', 'result', 'loser', 'location'] | [['1970', 'october 4', 'baltimore colts', '14 - 6', 'boston patriots', 'harvard stadium'], ['1970', 'october 25', 'baltimore colts', '27 - 3', 'boston patriots', 'memorial stadium ( baltimore )'], ['1971', 'october 3', 'baltimore colts', '23 - 3', 'new england patriots', 'schaefer stadium'], ['1971', 'december 19', 'new england patriots', '21 - 17', 'baltimore colts', 'memorial stadium ( baltimore )'], ['1972', 'november 6', 'baltimore colts', '24 - 17', 'new england patriots', 'schaefer stadium'], ['1972', 'november 26', 'baltimore colts', '31 - 0', 'new england patriots', 'memorial stadium ( baltimore )'], ['1973', 'october 7', 'new england patriots', '24 - 16', 'baltimore colts', 'schaefer stadium'], ['1973', 'december 16', 'baltimore colts', '18 - 13', 'new england patriots', 'memorial stadium ( baltimore )'], ['1974', 'october 6', 'new england patriots', '42 - 3', 'baltimore colts', 'schaefer stadium'], ['1974', 'november 24', 'new england patriots', '27 - 17', 'baltimore colts', 'memorial stadium ( baltimore )'], ['1975', 'october 19', 'new england patriots', '21 - 10', 'baltimore colts', 'schaefer stadium'], ['1975', 'december 21', 'baltimore colts', '34 - 21', 'new england patriots', 'memorial stadium ( baltimore )'], ['1976', 'september 12', 'baltimore colts', '27 - 13', 'new england patriots', 'schaefer stadium'], ['1976', 'november 14', 'new england patriots', '21 - 14', 'baltimore colts', 'memorial stadium ( baltimore )'], ['1977', 'october 23', 'new england patriots', '17 - 3', 'baltimore colts', 'schaefer stadium'], ['1977', 'december 18', 'baltimore colts', '30 - 24', 'new england patriots', 'memorial stadium ( baltimore )'], ['1978', 'september 18', 'baltimore colts', '34 - 27', 'new england patriots', 'schaefer stadium'], ['1978', 'november 26', 'new england patriots', '35 - 14', 'baltimore colts', 'memorial stadium ( baltimore )'], ['1979', 'october 28', 'baltimore colts', '31 - 26', 'new england patriots', 'memorial stadium ( baltimore )'], ['1979', 'november 18', 'new england patriots', '50 - 21', 'baltimore colts', 'schaefer stadium']] |
iran at the 2007 asian indoor games | https://en.wikipedia.org/wiki/Iran_at_the_2007_Asian_Indoor_Games | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14350710-31.html.csv | count | three competitors for iran at the 2007 asian indoor games did not advance to the final . | {'scope': 'all', 'criterion': 'equal', 'value': 'did not advance', 'result': '3', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'final', 'did not advance'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose final record fuzzily matches to did not advance .', 'tostr': 'filter_eq { all_rows ; final ; did not advance }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; final ; did not advance } }', 'tointer': 'select the rows whose final record fuzzily matches to did not advance . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; final ; did not advance } } ; 3 } = true', 'tointer': 'select the rows whose final record fuzzily matches to did not advance . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; final ; did not advance } } ; 3 } = true | select the rows whose final record fuzzily matches to did not advance . 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, 'final_5': 5, 'did not advance_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', 'final_5': 'final', 'did not advance_6': 'did not advance', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'final_5': [0], 'did not advance_6': [0], '3_7': [2]} | ['athlete', 'event', 'quarterfinal', 'semifinal', 'final'] | [['ali ekranpour', '63.5 kg', 'did not advance', 'did not advance', 'did not advance'], ['jalal motamedi', '67 kg', 'ng ( mac ) w 5 - 0', 'kahhorov ( uzb ) l 0 - 5', 'did not advance'], ['vahid roshani', '71 kg', 'jawad ( irq ) w 5 - 0', 'shetty ( ind ) w rsch', 'kadirkulov ( uzb ) l 1 - 4'], ['mostafa abdollahi', '75 kg', 'chu ( mac ) w knockout', 'el - kaissi ( lib ) w rsch', 'shukla ( ind ) w rsch'], ['yousef soltani', '81 kg', 'matsumoto ( jpn ) l 0 - 5', 'did not advance', 'did not advance']] |
united states house of representatives elections , 2000 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_2000 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341423-22.html.csv | ordinal | john conyers jr recorded the highest percentage ratio among all candidates of the 2000 house of representatives elections . | {'row': '10', 'col': '6', 'order': '1', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'candidates', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; candidates ; 1 }'}, 'incumbent'], 'result': 'john conyers jr', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent }'}, 'john conyers jr'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; john conyers jr } = true', 'tointer': 'select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is john conyers jr .'} | eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; john conyers jr } = true | select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is john conyers jr . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'candidates_5': 5, '1_6': 6, 'incumbent_7': 7, 'john conyers jr_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', 'candidates_5': 'candidates', '1_6': '1', 'incumbent_7': 'incumbent', 'john conyers jr_8': 'john conyers jr'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'candidates_5': [0], '1_6': [0], 'incumbent_7': [1], 'john conyers jr_8': [2]} | ['district', 'incumbent', 'party', 'first elected', 'results', 'candidates'] | [['michigan 1', 'bart stupak', 'democratic', '1992', 're - elected', 'bart stupak ( d ) 59 % chuck yob ( r ) 41 %'], ['michigan 2', 'pete hoekstra', 'republican', '1992', 're - elected', 'pete hoekstra ( r ) 65 % bob shrauger ( d ) 34 %'], ['michigan 3', 'vern ehlers', 'republican', '1993', 're - elected', 'vern ehlers ( r ) 65 % timothy steele ( d ) 34 %'], ['michigan 5', 'james barcia', 'democratic', '1992', 're - elected', 'james barcia ( d ) 75 % ronald actis ( r ) 24 %'], ['michigan 6', 'fred upton', 'republican', '1986', 're - elected', 'fred upton ( r ) 68 % james bupp ( d ) 30 %'], ['michigan 7', 'nick smith', 'republican', '1992', 're - elected', 'nick smith ( r ) 62 % jennie crittendon ( d ) 36 %'], ['michigan 9', 'dale kildee', 'democratic', '1976', 're - elected', 'dale kildee ( d ) 62 % grant garrett ( r ) 36 %'], ['michigan 10', 'david bonior', 'democratic', '1976', 're - elected', 'david bonior ( d ) 65 % tom turner ( r ) 34 %'], ['michigan 13', 'lynn rivers', 'democratic', '1994', 're - elected', 'lynn rivers ( d ) 65 % carl barry ( r ) 33 %'], ['michigan 14', 'john conyers jr', 'democratic', '1964', 're - elected', 'john conyers jr ( d ) 90 % william ashe ( r ) 10 %']] |
2008 - 09 detroit pistons season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Detroit_Pistons_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17325937-5.html.csv | aggregation | the average number of points the detroit pistons scored in the 2008-09 season was 97.3 . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '97.3', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'score'], 'result': '97.3', 'ind': 0, 'tostr': 'avg { all_rows ; score }'}, '97.3'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; score } ; 97.3 } = true', 'tointer': 'the average of the score record of all rows is 97.3 .'} | round_eq { avg { all_rows ; score } ; 97.3 } = true | the average of the score record of all rows is 97.3 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'score_4': 4, '97.3_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'score_4': 'score', '97.3_5': '97.3'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'score_4': [0], '97.3_5': [1]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['2', 'november 1', 'washington', 'w 117 - 109 ( ot )', 'richard hamilton ( 24 )', 'rasheed wallace ( 12 )', 'chauncey billups ( 8 )', 'the palace of auburn hills 22076', '2 - 0'], ['3', 'november 3', 'charlotte', 'w 101 - 83 ( ot )', 'richard hamilton ( 19 )', 'kwame brown ( 9 )', 'richard hamilton ( 5 )', 'time warner cable arena 11023', '3 - 0'], ['4', 'november 5', 'toronto', 'w 100 - 93 ( ot )', 'tayshaun prince ( 27 )', 'rasheed wallace ( 12 )', 'richard hamilton , rodney stuckey ( 5 )', 'air canada centre 18602', '4 - 0'], ['5', 'november 7', 'new jersey', 'l 96 - 103 ( ot )', 'allen iverson ( 24 )', 'tayshaun prince ( 11 )', 'allen iverson , rodney stuckey ( 6 )', 'izod center 17767', '4 - 1'], ['6', 'november 9', 'boston', 'l 76 - 88 ( ot )', 'tayshaun prince ( 23 )', 'rasheed wallace ( 11 )', 'allen iverson ( 4 )', 'the palace of auburn hills 22076', '4 - 2'], ['7', 'november 11', 'sacramento', 'w 100 - 92 ( ot )', 'allen iverson ( 30 )', 'tayshaun prince ( 11 )', 'allen iverson ( 9 )', 'arco arena 11423', '5 - 2'], ['8', 'november 13', 'golden state', 'w 107 - 102 ( ot )', 'richard hamilton ( 24 )', 'tayshaun prince ( 16 )', 'allen iverson ( 9 )', 'oracle arena 18477', '6 - 2'], ['9', 'november 14', 'la lakers', 'w 106 - 95 ( ot )', 'allen iverson , rasheed wallace ( 25 )', 'rasheed wallace ( 13 )', 'tayshaun prince ( 6 )', 'staples center 18997', '7 - 2'], ['10', 'november 16', 'phoenix', 'l 86 - 104 ( ot )', 'richard hamilton ( 19 )', 'rasheed wallace ( 9 )', 'allen iverson ( 7 )', 'us airways center 18422', '7 - 3'], ['11', 'november 19', 'cleveland', 'w 96 - 89 ( ot )', 'allen iverson ( 23 )', 'rasheed wallace ( 15 )', 'richard hamilton ( 5 )', 'the palace of auburn hills 22076', '8 - 3'], ['12', 'november 20', 'boston', 'l 80 - 98 ( ot )', 'allen iverson ( 16 )', 'kwame brown , tayshaun prince ( 7 )', 'allen iverson ( 4 )', 'td banknorth garden 18624', '8 - 4'], ['13', 'november 23', 'minnesota', 'l 80 - 106 ( ot )', 'tayshaun prince ( 20 )', 'rasheed wallace ( 10 )', 'will bynum , richard hamilton ( 6 )', 'the palace of auburn hills 22076', '8 - 5'], ['14', 'november 26', 'new york', 'w 110 - 96 ( ot )', 'richard hamilton ( 17 )', 'amir johnson ( 13 )', 'rodney stuckey ( 11 )', 'the palace of auburn hills 22076', '9 - 5'], ['15', 'november 28', 'milwaukee', 'w 107 - 97 ( ot )', 'allen iverson ( 17 )', 'jason maxiell ( 8 )', 'allen iverson ( 7 )', 'the palace of auburn hills 22076', '10 - 5']] |
united states house of representatives elections , 1968 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1968 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341738-34.html.csv | count | there were two districts in north carolina that did not have a current incumbent for the 1968 united states house of representatives elections . | {'scope': 'all', 'criterion': 'equal', 'value': 'none ( district created )', 'result': '2', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'none ( district created )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose incumbent record fuzzily matches to none ( district created ) .', 'tostr': 'filter_eq { all_rows ; incumbent ; none ( district created ) }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; incumbent ; none ( district created ) } }', 'tointer': 'select the rows whose incumbent record fuzzily matches to none ( district created ) . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; incumbent ; none ( district created ) } } ; 2 } = true', 'tointer': 'select the rows whose incumbent record fuzzily matches to none ( district created ) . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; incumbent ; none ( district created ) } } ; 2 } = true | select the rows whose incumbent record fuzzily matches to none ( district created ) . 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, 'incumbent_5': 5, 'none (district created)_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', 'incumbent_5': 'incumbent', 'none (district created)_6': 'none ( district created )', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'incumbent_5': [0], 'none (district created)_6': [0], '2_7': [2]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['north carolina 2', 'lawrence h fountain', 'democratic', '1952', 're - elected', 'lawrence h fountain ( d ) unopposed'], ['north carolina 2', 'james carson gardner redistricted from 4th', 'republican', '1966', 'retired to run for governor republican loss', 'lawrence h fountain ( d ) unopposed'], ['north carolina 4', 'nick galifianakis redistricted from 5th', 'democratic', '1966', 're - elected', 'nick galifianakis ( d ) 51.5 % fred steele ( r ) 48.5 %'], ['north carolina 5', 'none ( district created )', 'none ( district created )', 'none ( district created )', 'new seat republican gain', 'wilmer mizell ( r ) 52.4 % smith bagley ( d ) 47.6 %'], ['north carolina 7', 'alton lennon', 'democratic', '1956', 're - elected', 'alton lennon ( d ) unopposed'], ['north carolina 8', 'none ( district created )', 'none ( district created )', 'none ( district created )', 'new seat republican gain', 'earl b ruth ( r ) 51.2 % voit gilmore ( d ) 48.8 %'], ['north carolina 9', 'charles r jonas redistricted from 8th', 'republican', '1952', 're - elected', 'charles r jonas ( r ) unopposed']] |
2008 - 09 atlanta hawks season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Atlanta_Hawks_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17311759-9.html.csv | ordinal | the atlanta hawks ' game against orlando recorded the highest attendance of the 2008 - 09 season . | {'row': '2', 'col': '8', 'order': '1', 'col_other': '3', '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', 'location attendance', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; location attendance ; 1 }'}, 'team'], 'result': 'orlando', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; location attendance ; 1 } ; team }'}, 'orlando'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; location attendance ; 1 } ; team } ; orlando } = true', 'tointer': 'select the row whose location attendance record of all rows is 1st maximum . the team record of this row is orlando .'} | eq { hop { nth_argmax { all_rows ; location attendance ; 1 } ; team } ; orlando } = true | select the row whose location attendance record of all rows is 1st maximum . the team record of this row is orlando . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'location attendance_5': 5, '1_6': 6, 'team_7': 7, 'orlando_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', 'location attendance_5': 'location attendance', '1_6': '1', 'team_7': 'team', 'orlando_8': 'orlando'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'location attendance_5': [0], '1_6': [0], 'team_7': [1], 'orlando_8': [2]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['76', 'april 3', 'boston', 'l 92 - 104 ( ot )', 'ronald murray ( 21 )', 'josh smith ( 10 )', 'mike bibby ( 6 )', 'td banknorth garden 18624', '43 - 33'], ['77', 'april 4', 'orlando', 'l 82 - 88 ( ot )', 'joe johnson ( 21 )', 'al horford ( 13 )', 'mike bibby ( 5 )', 'philips arena 19608', '43 - 34'], ['78', 'april 7', 'toronto', 'w 118 - 110 ( ot )', 'joe johnson , josh smith ( 25 )', 'al horford ( 12 )', 'mike bibby ( 10 )', 'air canada centre 17613', '44 - 34'], ['79', 'april 8', 'milwaukee', 'w 113 - 105 ( ot )', 'joe johnson ( 30 )', 'al horford ( 9 )', 'mike bibby ( 8 )', 'bradley center 13073', '45 - 34'], ['80', 'april 10', 'indiana', 'w 122 - 118 ( ot )', 'josh smith ( 30 )', 'al horford ( 15 )', 'mike bibby ( 9 )', 'philips arena 17222', '46 - 34'], ['81', 'april 14', 'miami', 'w 81 - 79 ( ot )', 'ronald murray ( 17 )', 'mario west ( 9 )', 'ronald murray ( 5 )', 'philips arena 18179', '47 - 34']] |
adriano leite ribeiro | https://en.wikipedia.org/wiki/Adriano_Leite_Ribeiro | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1142467-2.html.csv | superlative | adriano leite ribeiro 's highest scoring season was in 2005-2006 with 8 goals . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '9', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '3', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'goals'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; goals }'}, 'season'], 'result': '2005 - 2006', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; goals } ; season }'}, '2005 - 2006'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; goals } ; season } ; 2005 - 2006 } = true', 'tointer': 'select the row whose goals record of all rows is maximum . the season record of this row is 2005 - 2006 .'} | eq { hop { argmax { all_rows ; goals } ; season } ; 2005 - 2006 } = true | select the row whose goals record of all rows is maximum . the season record of this row is 2005 - 2006 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'goals_5': 5, 'season_6': 6, '2005 - 2006_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'goals_5': 'goals', 'season_6': 'season', '2005 - 2006_7': '2005 - 2006'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'goals_5': [0], 'season_6': [1], '2005 - 2006_7': [2]} | ['national team', 'club', 'season', 'apps', 'goals'] | [['brazil', 'flamengo', '2000', '1', '0'], ['brazil', 'flamengo', '2001', '0', '0'], ['brazil', 'internazionale', '2001 - 2002', '0', '0'], ['brazil', 'fiorentina', '2001 - 2002', '0', '0'], ['brazil', 'parma', '2002 - 2003', '5', '3'], ['brazil', 'parma', '2003 - 2004', '1', '0'], ['brazil', 'internazionale', '2003 - 2004', '6', '7'], ['brazil', 'internazionale', '2004 - 2005', '12', '7'], ['brazil', 'internazionale', '2005 - 2006', '11', '8'], ['brazil', 'internazionale', '2006 - 2007', '1', '0'], ['brazil', 'internazionale', '2007 - 2008', '0', '0'], ['brazil', 'são paulo', '2008', '4', '0'], ['brazil', 'internazionale', '2008 - 2009', '3', '2'], ['brazil', 'flamengo', '2009', '3', '0'], ['brazil', 'flamengo', '2010', '1', '0'], ['total', 'total', 'total', '48', '27']] |
list of cities , towns and villages in vojvodina | https://en.wikipedia.org/wiki/List_of_cities%2C_towns_and_villages_in_Vojvodina | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2562572-2.html.csv | superlative | novi sad is the urban settlement in vojvodina that had the highest population in 2002 . | {'scope': 'all', 'col_superlative': '6', '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', 'population ( 2002 )'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; population ( 2002 ) }'}, 'urban settlement'], 'result': 'novi sad', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; population ( 2002 ) } ; urban settlement }'}, 'novi sad'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; population ( 2002 ) } ; urban settlement } ; novi sad } = true', 'tointer': 'select the row whose population ( 2002 ) record of all rows is maximum . the urban settlement record of this row is novi sad .'} | eq { hop { argmax { all_rows ; population ( 2002 ) } ; urban settlement } ; novi sad } = true | select the row whose population ( 2002 ) record of all rows is maximum . the urban settlement record of this row is novi sad . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'population (2002)_5': 5, 'urban settlement_6': 6, 'novi sad_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'population (2002)_5': 'population ( 2002 )', 'urban settlement_6': 'urban settlement', 'novi sad_7': 'novi sad'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'population (2002)_5': [0], 'urban settlement_6': [1], 'novi sad_7': [2]} | ['urban settlement', 'cyrillic name', 'city / municipality', 'district', 'population ( 1991 )', 'population ( 2002 )', 'population ( 2011 )'] | [['bač', 'бач', 'bač', 'south bačka', '6046', '6087', '5399'], ['bačka palanka', 'бачка паланка', 'bačka palanka', 'south bačka', '26780', '29449', '28239'], ['bački jarak', 'бачки јарак', 'temerin', 'south bačka', '5426', '6049', '5687'], ['bački petrovac', 'бачки петровац', 'bački petrovac', 'south bačka', '7236', '6727', '6155'], ['bečej', 'бечеј', 'bečej', 'south bačka', '26634', '25774', '23895'], ['beočin', 'беочин', 'beočin', 'south bačka', '7873', '8058', '7839'], ['futog', 'футог', 'novi sad', 'south bačka', '16048', '18582', '18641'], ['novi sad', 'нови сад', 'novi sad', 'south bačka', '179626', '191405', '250439'], ['petrovaradin', 'петроварадин', 'petrovaradin , novi sad', 'south bačka', '11285', '13973', '14810'], ['srbobran', 'србобран', 'srbobran', 'south bačka', '12798', '13091', '12009'], ['sremska kamenica', 'сремска каменица', 'petrovaradin , novi sad', 'south bačka', '7955', '11205', '12273'], ['sremski karlovci', 'сремски карловци', 'sremski karlovci', 'south bačka', '7534', '8839', '8750'], ['temerin', 'темерин', 'temerin', 'south bačka', '16971', '19216', '19661'], ['titel', 'тител', 'titel', 'south bačka', '6007', '5894', '5294'], ['vrbas', 'врбас', 'vrbas', 'south bačka', '25858', '25907', '24112']] |
jean - pierre beltoise | https://en.wikipedia.org/wiki/Jean-Pierre_Beltoise | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1226341-2.html.csv | majority | in most of the years that jean - pierre beltoise competed , he was ranked lowered than 10th . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '10', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'rank', '10'], 'result': True, 'ind': 0, 'tointer': 'for the rank records of all rows , most of them are greater than 10 .', 'tostr': 'most_greater { all_rows ; rank ; 10 } = true'} | most_greater { all_rows ; rank ; 10 } = true | for the rank records of all rows , most of them are greater than 10 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'rank_3': 3, '10_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'rank_3': 'rank', '10_4': '10'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'rank_3': [0], '10_4': [0]} | ['year', 'class', 'team', 'points', 'rank', 'wins'] | [['1962', '250cc', 'moto morini', '2', '20th', '0'], ['1963', '50cc', 'kreidler', '3', '11th', '0'], ['1963', '125cc', 'bultaco', '1', '20th', '0'], ['1964', '50cc', 'kreidler', '6', '6th', '0'], ['1964', '125cc', 'bultaco', '4', '13th', '0']] |
2007 bombardier learjet 550 | https://en.wikipedia.org/wiki/2007_Bombardier_Learjet_550 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17319931-1.html.csv | superlative | sam hornish , jr was the driver that led the highest amount of laps in the 2007 bombardier learjet 550 . | {'scope': 'all', 'col_superlative': '8', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '3', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'laps led'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; laps led }'}, 'driver'], 'result': 'sam hornish , jr', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; laps led } ; driver }'}, 'sam hornish , jr'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; laps led } ; driver } ; sam hornish , jr } = true', 'tointer': 'select the row whose laps led record of all rows is maximum . the driver record of this row is sam hornish , jr .'} | eq { hop { argmax { all_rows ; laps led } ; driver } ; sam hornish , jr } = true | select the row whose laps led record of all rows is maximum . the driver record of this row is sam hornish , jr . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'laps led_5': 5, 'driver_6': 6, 'sam hornish , jr_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'laps led_5': 'laps led', 'driver_6': 'driver', 'sam hornish , jr_7': 'sam hornish , jr'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'laps led_5': [0], 'driver_6': [1], 'sam hornish , jr_7': [2]} | ['fin pos', 'car no', 'driver', 'team', 'laps', 'time / retired', 'grid', 'laps led', 'points'] | [['1', '6', 'sam hornish , jr', 'team penske', '228', '1:52:15.2873', '2', '159', '50 + 3'], ['2', '11', 'tony kanaan', 'andretti green', '228', '+ 0.0786', '4', '1', '40'], ['3', '7', 'danica patrick', 'andretti green', '228', '+ 0.3844', '6', '2', '35'], ['4', '27', 'dario franchitti', 'andretti green', '228', '+ 3.9765', '3', '0', '32'], ['5', '4', 'vitor meira', 'panther racing', '228', '+ 4.0019', '13', '3', '30'], ['6', '17', 'jeff simmons', 'rahal letterman', '228', '+ 4.6340', '8', '5', '28'], ['7', '8', 'scott sharp', 'rahal letterman', '227', '+ 1 lap', '1', '0', '26'], ['8', '15', 'buddy rice', 'dreyer & reinbold racing', '225', '+ 3 laps', '16', '0', '24'], ['9', '55', 'kosuke matsuura', 'panther racing', '225', '+ 3 laps', '15', '0', '22'], ['10', '5', 'sarah fisher', 'dreyer & reinbold racing', '221', '+ 7 laps', '18', '0', '20'], ['11', '23', 'milka duno ( r )', 'samax motorsport', '221', '+ 7 laps', '19', '0', '19'], ['12', '9', 'scott dixon', 'target chip ganassi', '206', '+ 22 laps', '7', '6', '18'], ['13', '14', 'darren manning', 'aj foyt racing', '200', 'mechanical', '11', '0', '17'], ['14', '2', 'tomas scheckter', 'vision racing', '199', '+ 29 laps', '9', '0', '16'], ['15', '10', 'dan wheldon', 'target chip ganassi', '196', 'collision', '5', '52', '15'], ['16', '3', 'hãlio castroneves', 'team penske', '196', 'collision', '10', '0', '14'], ['17', '22', 'a j foyt iv', 'vision racing', '195', 'tire', '17', '0', '13'], ['18', '20', 'ed carpenter', 'vision racing', '195', 'collision', '20', '0', '12'], ['19', '26', 'marco andretti', 'andretti green', '140', 'mechanical', '12', '0', '12'], ['20', '19', 'jon herb', 'racing professionals', '44', 'accident', '14', '0', '12']] |
ohio river valley conference | https://en.wikipedia.org/wiki/Ohio_River_Valley_Conference | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18717975-2.html.csv | ordinal | north ( madison ) was the first school to leave the ohio river valley conference . | {'row': '2', 'col': '6', 'order': '1', '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', 'year left', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; year left ; 1 }'}, 'school'], 'result': 'north ( madison )', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; year left ; 1 } ; school }'}, 'north ( madison )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; year left ; 1 } ; school } ; north ( madison ) } = true', 'tointer': 'select the row whose year left record of all rows is 1st minimum . the school record of this row is north ( madison ) .'} | eq { hop { nth_argmin { all_rows ; year left ; 1 } ; school } ; north ( madison ) } = true | select the row whose year left record of all rows is 1st minimum . the school record of this row is north ( madison ) . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'year left_5': 5, '1_6': 6, 'school_7': 7, 'north (madison)_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 left_5': 'year left', '1_6': '1', 'school_7': 'school', 'north (madison)_8': 'north ( madison )'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'year left_5': [0], '1_6': [0], 'school_7': [1], 'north (madison)_8': [2]} | ['school', 'location', 'mascot', 'county', 'year joined', 'year left', 'conference joined'] | [['hanover', 'hanover', 'bulldogs', '39 jefferson', '1952', '1960', 'none ( consolidated into southwestern )'], ['north ( madison )', 'madison', 'tigers', '39 jefferson', '1952', '1953', 'none ( colsolidated into madison )'], ['osgood', 'osgood', 'cowboys', '69 ripley', '1952', '1960', 'none ( consolidated into jac - cen - del )'], ['versailles', 'versailles', 'lions', '69 ripley', '1952', '1966', 'none ( consolidated into south ripley )'], ['dillsboro', 'dillsboro', 'bulldogs', '15 dearborn', '1953', '1978', 'none ( consolidated into south dearborn )'], ['moores hill', 'moores hill', 'knights', '15 dearborn', '1953', '1978', 'none ( consolidated into south dearborn )'], ['sunman', 'sunman', 'trojans', '69 ripley', '1953', '1973', 'none ( consolidated into east central )'], ['vevay', 'vevay', 'warriors', '78 switzerland', '1953', '1968', 'none ( consolidated into switzerland county )']] |
mohammed nasser shakroun | https://en.wikipedia.org/wiki/Mohammed_Nasser_Shakroun | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13607991-4.html.csv | ordinal | mohammed nasser shakroun scored his second international goal at the bahrain national stadium . | {'row': '2', 'col': '1', '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', 'date', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; date ; 2 }'}, 'venue'], 'result': 'bahrain national stadium , manama', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; date ; 2 } ; venue }'}, 'bahrain national stadium , manama'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; date ; 2 } ; venue } ; bahrain national stadium , manama } = true', 'tointer': 'select the row whose date record of all rows is 2nd minimum . the venue record of this row is bahrain national stadium , manama .'} | eq { hop { nth_argmin { all_rows ; date ; 2 } ; venue } ; bahrain national stadium , manama } = true | select the row whose date record of all rows is 2nd minimum . the venue record of this row is bahrain national stadium , manama . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'date_5': 5, '2_6': 6, 'venue_7': 7, 'bahrain national stadium , manama_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', 'date_5': 'date', '2_6': '2', 'venue_7': 'venue', 'bahrain national stadium , manama_8': 'bahrain national stadium , manama'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'date_5': [0], '2_6': [0], 'venue_7': [1], 'bahrain national stadium , manama_8': [2]} | ['date', 'venue', 'score', 'result', 'competition'] | [['26 march 2005', 'telstra stadium , sydney', '1 - 0', '1 - 2', 'friendly match'], ['7 august 2005', 'bahrain national stadium , manama', '1 - 2', '2 - 2', 'friendly match'], ['13 august 2005', 'tsirion stadium , limassol', '1 - 0', '2 - 1', 'friendly match'], ['15 march 2006', 'prince abdullah al - faisal stadium , jeddah', '2 - 0', '2 - 2', 'friendly match'], ['17 august 2006', 'king abdullah stadium , amman', '3 - 0', '3 - 0', 'friendly match'], ['27 december 2008', 'tahnoun bin mohamed stadium , al ain', '1 - 1', '2 - 2', 'friendly match']] |
baltimore clippers | https://en.wikipedia.org/wiki/Baltimore_Clippers | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2817196-1.html.csv | aggregation | between 1973 and 1976 , the baltimore clippers won a total of 77 games . | {'scope': 'subset', 'col': '3', 'type': 'sum', 'result': '77', 'subset': {'col': '1', 'criterion': 'greater_than_eq', 'value': '1973'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_greater_eq', 'args': ['all_rows', 'season', '1973'], 'result': None, 'ind': 0, 'tostr': 'filter_greater_eq { all_rows ; season ; 1973 }', 'tointer': 'select the rows whose season record is greater than or equal to 1973 .'}, 'won'], 'result': '77', 'ind': 1, 'tostr': 'sum { filter_greater_eq { all_rows ; season ; 1973 } ; won }'}, '77'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_greater_eq { all_rows ; season ; 1973 } ; won } ; 77 } = true', 'tointer': 'select the rows whose season record is greater than or equal to 1973 . the sum of the won record of these rows is 77 .'} | round_eq { sum { filter_greater_eq { all_rows ; season ; 1973 } ; won } ; 77 } = true | select the rows whose season record is greater than or equal to 1973 . the sum of the won record of these rows is 77 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_greater_eq_0': 0, 'all_rows_4': 4, 'season_5': 5, '1973_6': 6, 'won_7': 7, '77_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_greater_eq_0': 'filter_greater_eq', 'all_rows_4': 'all_rows', 'season_5': 'season', '1973_6': '1973', 'won_7': 'won', '77_8': '77'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_greater_eq_0': [1], 'all_rows_4': [0], 'season_5': [0], '1973_6': [0], 'won_7': [1], '77_8': [2]} | ['season', 'games', 'won', 'lost', 'tied', 'points', 'goals for', 'goals against', 'standing', 'head coaches'] | [['1962 - 63', '72', '35', '30', '7', '77', '226', '244', '3rd , east', 'red sullivan / aldo guidolin'], ['1963 - 64', '72', '32', '37', '3', '67', '200', '220', '4th , east', 'aldo guidolin'], ['1964 - 65', '72', '35', '32', '5', '75', '275', '249', '3rd , east', 'john crawford'], ['1965 - 66', '72', '27', '43', '2', '56', '212', '254', '4th , east', 'john crawford , terry reardon'], ['1966 - 67', '72', '35', '27', '10', '80', '252', '247', '2nd , east', 'terry reardon'], ['1967 - 68', '72', '28', '34', '10', '66', '236', '255', '4th , east', 'terry reardon'], ['1968 - 69', '74', '33', '34', '7', '73', '266', '257', '2nd , east', 'aldo guidolin'], ['1969 - 70', '72', '25', '30', '17', '67', '230', '252', '3rd , west', 'rudy migay'], ['1970 - 71', '72', '40', '23', '9', '89', '263', '224', '1st , west', 'terry reardon'], ['1971 - 72', '76', '34', '31', '11', '79', '240', '249', '1st , west', 'terry reardon / jim morrison'], ['1972 - 73', '76', '17', '48', '11', '45', '210', '315', '6th , west', 'terry reardon / jim morrison'], ['1973 - 74', '76', '42', '24', '10', '94', '310', '232', '1st , south', 'terry reardon / jim morrison'], ['1974 - 75', '46', '14', '22', '10', '38', '136', '180', '5th , south', 'terry reardon / kent douglas'], ['1975 - 76', '76', '21', '48', '7', '49', '238', '316', '4th , south', 'terry reardon / kent douglas']] |
1991 - 92 in argentine football | https://en.wikipedia.org/wiki/1991%E2%80%9392_in_Argentine_football | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14390413-1.html.csv | ordinal | the boca juniors team recorded the 2nd highest average in the 1991 - 92 argentine football season . | {'row': '2', 'col': '2', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'average', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; average ; 2 }'}, 'team'], 'result': 'boca juniors', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; average ; 2 } ; team }'}, 'boca juniors'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; average ; 2 } ; team } ; boca juniors } = true', 'tointer': 'select the row whose average record of all rows is 2nd maximum . the team record of this row is boca juniors .'} | eq { hop { nth_argmax { all_rows ; average ; 2 } ; team } ; boca juniors } = true | select the row whose average record of all rows is 2nd maximum . the team record of this row is boca juniors . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'average_5': 5, '2_6': 6, 'team_7': 7, 'boca juniors_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', 'average_5': 'average', '2_6': '2', 'team_7': 'team', 'boca juniors_8': 'boca juniors'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'average_5': [0], '2_6': [0], 'team_7': [1], 'boca juniors_8': [2]} | ['team', 'average', 'points', 'played', '1989 - 90', '1990 - 91', '1991 - 1992'] | [['river plate', '1.342', '153', '114', '53', '45', '55'], ['boca juniors', '1.263', '144', '114', '43', '51', '50'], ['vélez sársfield', '1.184', '135', '114', '42', '45', '48'], ["newell 's old boys", '1.123', '128', '114', '36', '48', '44'], ['independiente', '1.070', '122', '114', '46', '40', '36'], ['racing club', '1.035', '118', '114', '39', '40', '39'], ['huracán', '1.026', '78', '76', 'n / a', '40', '38'], ['rosario central', '1.018', '116', '114', '43', '39', '34'], ['ferro carril oeste', '1.000', '114', '114', '39', '38', '37'], ['san lorenzo', '1.000', '114', '114', '35', '45', '34'], ['gimnasia de la plata', '0.991', '113', '114', '39', '33', '41'], ['platense', '0.991', '113', '114', '36', '35', '42'], ['argentinos juniors', '0.956', '109', '114', '38', '36', '35'], ['deportivo mandiyú', '0.939', '107', '114', '36', '38', '33'], ['belgrano de córdoba', '0.921', '35', '38', 'n / a', 'n / a', '35'], ['deportivo español', '0.912', '104', '114', '31', '28', '45'], ['estudiantes de la plata', '0.895', '102', '114', '34', '39', '29'], ['talleres de córdoba', '0.895', '102', '114', '36', '29', '37'], ['unión de santa fe', '0.825', '94', '114', '36', '31', '27']] |
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/2-11545282-13.html.csv | majority | all of the players on the utah jazz all - time roster are from the united states . | {'scope': 'all', 'col': '2', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'united states', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'nationality', 'united states'], 'result': True, 'ind': 0, 'tointer': 'for the nationality records of all rows , all of them fuzzily match to united states .', 'tostr': 'all_eq { all_rows ; nationality ; united states } = true'} | all_eq { all_rows ; nationality ; united states } = true | for the nationality records of all rows , all of them fuzzily match to united states . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'nationality_3': 3, 'united states_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'nationality_3': 'nationality', 'united states_4': 'united states'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'nationality_3': [0], 'united states_4': [0]} | ['player', 'nationality', 'position', 'years for jazz', 'school / club team'] | [['jeff malone', 'united states', 'shooting guard', '1991 - 94', 'mississippi state'], ['karl malone', 'united states', 'power forward', '1985 - 03', 'louisiana tech'], ['danny manning', 'united states', 'combo forward', '2000 - 01', 'kansas'], ['pace mannion', 'united states', 'guard - forward', '1984 - 86', 'utah'], ['pete maravich', 'united states', 'point guard', '1974 - 80', 'lsu'], ['donyell marshall', 'united states', 'forward', '2000 - 02', 'connecticut'], ['tony massenburg', 'united states', 'forward', '2002 - 03', 'maryland'], ['keith mcleod', 'united states', 'guard', '2004 - 06', 'bowling green'], ['jim mcelroy', 'united states', 'guard', '1975 - 79', 'central michigan'], ['billy mckinney', 'united states', 'guard', '1980 - 81', 'northwestern'], ['joe meriweather', 'united states', 'forward - center', '1977 - 79', 'southern illinois'], ['c j miles', 'united states', 'guard - forward', '2005 - present', 'skyline hs'], ['dick miller', 'united states', 'forward', '1980 - 81', 'toledo'], ['paul millsap', 'united states', 'power forward', '2006present', 'louisiana tech'], ['mikki moore', 'united states', 'forward - center', '2003 - 04', 'nebraska'], ['otto moore', 'united states', 'forward - center', '1974 - 77', 'texas - pan american'], ['darren morningstar', 'united states', 'center', '1994', 'pittsburgh'], ['chris morris', 'united states', 'small forward', '1995 - 98', 'auburn'], ['chris munk', 'united states', 'forward', '1990 - 91', 'usc'], ['eric murdock', 'united states', 'guard', '1991 - 92', 'providence']] |
1971 vfl season | https://en.wikipedia.org/wiki/1971_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10826072-22.html.csv | ordinal | hawthorn had the 2nd highest home team score in the 1971 vfl season . | {'row': '1', 'col': '2', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'home team score', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; home team score ; 2 }'}, 'home team'], 'result': 'hawthorn', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; home team score ; 2 } ; home team }'}, 'hawthorn'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; home team score ; 2 } ; home team } ; hawthorn } = true', 'tointer': 'select the row whose home team score record of all rows is 2nd maximum . the home team record of this row is hawthorn .'} | eq { hop { nth_argmax { all_rows ; home team score ; 2 } ; home team } ; hawthorn } = true | select the row whose home team score record of all rows is 2nd maximum . the home team record of this row is hawthorn . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'home team score_5': 5, '2_6': 6, 'home team_7': 7, 'hawthorn_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', 'home team score_5': 'home team score', '2_6': '2', 'home team_7': 'home team', 'hawthorn_8': 'hawthorn'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'home team score_5': [0], '2_6': [0], 'home team_7': [1], 'hawthorn_8': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['hawthorn', '18.16 ( 124 )', 'melbourne', '8.17 ( 65 )', 'glenferrie oval', '14809', '28 august 1971'], ['footscray', '10.14 ( 74 )', 'st kilda', '12.18 ( 90 )', 'western oval', '16707', '28 august 1971'], ['essendon', '12.12 ( 84 )', 'fitzroy', '13.17 ( 95 )', 'windy hill', '12865', '28 august 1971'], ['carlton', '16.10 ( 106 )', 'collingwood', '13.9 ( 87 )', 'princes park', '32000', '28 august 1971'], ['south melbourne', '19.17 ( 131 )', 'north melbourne', '8.11 ( 59 )', 'lake oval', '9307', '28 august 1971'], ['richmond', '16.14 ( 110 )', 'geelong', '14.18 ( 102 )', 'mcg', '36423', '28 august 1971']] |
2003 u.s. open ( golf ) | https://en.wikipedia.org/wiki/2003_U.S._Open_%28golf%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16292316-1.html.csv | comparative | tom watson had won a u.s. open ( golf ) championship earlier than retief goosen . | {'row_1': '3', 'row_2': '4', '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', 'player', 'tom watson'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to tom watson .', 'tostr': 'filter_eq { all_rows ; player ; tom watson }'}, 'year ( s ) won'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; tom watson } ; year ( s ) won }', 'tointer': 'select the rows whose player record fuzzily matches to tom watson . take the year ( s ) won record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'retief goosen'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to retief goosen .', 'tostr': 'filter_eq { all_rows ; player ; retief goosen }'}, 'year ( s ) won'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; retief goosen } ; year ( s ) won }', 'tointer': 'select the rows whose player record fuzzily matches to retief goosen . take the year ( s ) won record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; player ; tom watson } ; year ( s ) won } ; hop { filter_eq { all_rows ; player ; retief goosen } ; year ( s ) won } } = true', 'tointer': 'select the rows whose player record fuzzily matches to tom watson . take the year ( s ) won record of this row . select the rows whose player record fuzzily matches to retief goosen . take the year ( s ) won record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; player ; tom watson } ; year ( s ) won } ; hop { filter_eq { all_rows ; player ; retief goosen } ; year ( s ) won } } = true | select the rows whose player record fuzzily matches to tom watson . take the year ( s ) won record of this row . select the rows whose player record fuzzily matches to retief goosen . take the year ( s ) won record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'player_7': 7, 'tom watson_8': 8, 'year (s) won_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'retief goosen_12': 12, 'year (s) won_13': 13} | {'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'player_7': 'player', 'tom watson_8': 'tom watson', 'year (s) won_9': 'year ( s ) won', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'retief goosen_12': 'retief goosen', 'year (s) won_13': 'year ( s ) won'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'tom watson_8': [0], 'year (s) won_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'retief goosen_12': [1], 'year (s) won_13': [3]} | ['player', 'country', 'year ( s ) won', 'total', 'to par', 'finish'] | [['ernie els', 'south africa', '1994 , 1997', '280', 'e', 't5'], ['tiger woods', 'united states', '2000 , 2002', '283', '+ 3', 't20'], ['tom watson', 'united states', '1982', '284', '+ 4', 't28'], ['retief goosen', 'south africa', '2001', '286', '+ 6', 't42'], ['lee janzen', 'united states', '1993 , 1998', '289', '+ 9', 't55']] |
list of highest - grossing bollywood films | https://en.wikipedia.org/wiki/List_of_highest-grossing_Bollywood_films | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11872185-1.html.csv | superlative | ek tha tiger was the highest-grossing bollywood film of 2012 . | {'scope': 'subset', 'col_superlative': '4', 'row_superlative': '3', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': {'col': '3', 'criterion': 'equal', 'value': '2012'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'year', '2012'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; year ; 2012 }', 'tointer': 'select the rows whose year record is equal to 2012 .'}, 'worldwide gross'], 'result': None, 'ind': 1, 'tostr': 'argmax { filter_eq { all_rows ; year ; 2012 } ; worldwide gross }'}, 'movie'], 'result': 'ek tha tiger', 'ind': 2, 'tostr': 'hop { argmax { filter_eq { all_rows ; year ; 2012 } ; worldwide gross } ; movie }'}, 'ek tha tiger'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { argmax { filter_eq { all_rows ; year ; 2012 } ; worldwide gross } ; movie } ; ek tha tiger } = true', 'tointer': 'select the rows whose year record is equal to 2012 . select the row whose worldwide gross record of these rows is maximum . the movie record of this row is ek tha tiger .'} | eq { hop { argmax { filter_eq { all_rows ; year ; 2012 } ; worldwide gross } ; movie } ; ek tha tiger } = true | select the rows whose year record is equal to 2012 . select the row whose worldwide gross record of these rows is maximum . the movie record of this row is ek tha tiger . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'argmax_1': 1, 'filter_eq_0': 0, 'all_rows_5': 5, 'year_6': 6, '2012_7': 7, 'worldwide gross_8': 8, 'movie_9': 9, 'ek tha tiger_10': 10} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'argmax_1': 'argmax', 'filter_eq_0': 'filter_eq', 'all_rows_5': 'all_rows', 'year_6': 'year', '2012_7': '2012', 'worldwide gross_8': 'worldwide gross', 'movie_9': 'movie', 'ek tha tiger_10': 'ek tha tiger'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'argmax_1': [2], 'filter_eq_0': [1], 'all_rows_5': [0], 'year_6': [0], '2012_7': [0], 'worldwide gross_8': [1], 'movie_9': [2], 'ek tha tiger_10': [3]} | ['rank', 'movie', 'year', 'worldwide gross', 'director', 'verdict'] | [['1', '3 idiots', '2009', '392 crore', 'rajkumar hirani', 'all time blockbuster'], ['2', 'chennai express', '2013', '314 crore', 'rohit shetty', 'blockbuster'], ['3', 'ek tha tiger', '2012', '310 crore', 'kabir khan', 'blockbuster'], ['4', 'yeh jawaani hai deewani', '2013', '301 crore', 'ayan mukerji', 'blockbuster'], ['5', 'dabangg 2', '2012', '251 crore', 'arbaaz khan', 'blockbuster'], ['6', 'bodyguard', '2011', '230 crore', 'siddique', 'blockbuster'], ['7', 'dabangg', '2010', '215 crore', 'abhinav singh kashyap', 'all time blockbuster'], ['8', 'jab tak hai jaan', '2012', '211 crore', 'yash chopra', 'super hit'], ['9', 'don 2', '2011', '206 crore', 'farhan akhtar', 'super hit'], ['10', 'raone', '2011', '202 crore', 'anubhav sinha', 'super hit']] |
united states house of representatives elections , 1950 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1950 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342198-36.html.csv | majority | all of the incumbents in the 1950 house of representatives elections were from the democratic party . | {'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'democratic', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'party', 'democratic'], 'result': True, 'ind': 0, 'tointer': 'for the party records of all rows , all of them fuzzily match to democratic .', 'tostr': 'all_eq { all_rows ; party ; democratic } = true'} | all_eq { all_rows ; party ; democratic } = true | for the party records of all rows , all of them fuzzily match to democratic . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'party_3': 3, 'democratic_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'party_3': 'party', 'democratic_4': 'democratic'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'party_3': [0], 'democratic_4': [0]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['oklahoma 1', 'dixie gilmer', 'democratic', '1948', 'lost re - election republican gain', 'george b schwabe ( r ) 52.9 % dixie gilmer ( d ) 47.1 %'], ['oklahoma 2', 'william g stigler', 'democratic', '1944', 're - elected', 'william g stigler ( d ) 66.2 % cleo crain ( r ) 33.8 %'], ['oklahoma 3', 'carl albert', 'democratic', '1946', 're - elected', 'carl albert ( d ) 82.8 % charles powell ( r ) 17.2 %'], ['oklahoma 4', 'tom steed', 'democratic', '1948', 're - elected', 'tom steed ( d ) 68.1 % glenn o young ( r ) 31.9 %'], ['oklahoma 5', 'a s mike monroney', 'democratic', '1938', 'retired to run for u s senate democratic hold', 'john jarman ( d ) 58.8 % c e barnes ( r ) 41.2 %'], ['oklahoma 6', 'toby morris', 'democratic', '1946', 're - elected', 'toby morris ( d ) 67.1 % george campbell ( r ) 32.9 %']] |
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 | superlative | the new york jets scored the most points against the dolphins . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '16', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '3', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'opponents'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; opponents }'}, 'opponent'], 'result': 'new york jets', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; opponents } ; opponent }'}, 'new york jets'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; opponents } ; opponent } ; new york jets } = true', 'tointer': 'select the row whose opponents record of all rows is maximum . the opponent record of this row is new york jets .'} | eq { hop { argmax { all_rows ; opponents } ; opponent } ; new york jets } = true | select the row whose opponents record of all rows is maximum . the opponent record of this row is new york jets . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'opponents_5': 5, 'opponent_6': 6, 'new york jets_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'opponents_5': 'opponents', 'opponent_6': 'opponent', 'new york jets_7': 'new york jets'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'opponents_5': [0], 'opponent_6': [1], 'new york jets_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']] |
1963 vfl season | https://en.wikipedia.org/wiki/1963_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10783853-8.html.csv | superlative | essendon had the highest scoring game out of all the 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': 'essendon', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; home team score } ; home team }'}, 'essendon'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; home team score } ; home team } ; essendon } = true', 'tointer': 'select the row whose home team score record of all rows is maximum . the home team record of this row is essendon .'} | eq { hop { argmax { all_rows ; home team score } ; home team } ; essendon } = true | select the row whose home team score record of all rows is maximum . the home team record of this row is essendon . | 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, 'essendon_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', 'essendon_7': 'essendon'} | {'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], 'essendon_7': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['essendon', '13.11 ( 89 )', 'richmond', '7.5 ( 47 )', 'windy hill', '21200', '8 june 1963'], ['carlton', '6.8 ( 44 )', 'collingwood', '6.10 ( 46 )', 'princes park', '38698', '8 june 1963'], ['st kilda', '8.13 ( 61 )', 'hawthorn', '9.11 ( 65 )', 'junction oval', '34900', '8 june 1963'], ['footscray', '6.16 ( 52 )', 'south melbourne', '5.9 ( 39 )', 'western oval', '22950', '10 june 1963'], ['fitzroy', '2.11 ( 23 )', 'north melbourne', '6.15 ( 51 )', 'brunswick street oval', '13400', '10 june 1963'], ['melbourne', '11.16 ( 82 )', 'geelong', '4.11 ( 35 )', 'mcg', '81550', '10 june 1963']] |
arkansas rimrockers | https://en.wikipedia.org/wiki/Arkansas_RimRockers | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1806054-1.html.csv | comparative | the arkansas rimrockers recorded a higher number of wins in the 2005-06 season than they did in the 2006-07 season . | {'row_1': '3', 'row_2': '4', 'col': '4', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'season', '2005 - 06'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose season record fuzzily matches to 2005 - 06 .', 'tostr': 'filter_eq { all_rows ; season ; 2005 - 06 }'}, 'wins'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; season ; 2005 - 06 } ; wins }', 'tointer': 'select the rows whose season record fuzzily matches to 2005 - 06 . take the wins record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'season', '2006 - 07'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose season record fuzzily matches to 2006 - 07 .', 'tostr': 'filter_eq { all_rows ; season ; 2006 - 07 }'}, 'wins'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; season ; 2006 - 07 } ; wins }', 'tointer': 'select the rows whose season record fuzzily matches to 2006 - 07 . take the wins record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; season ; 2005 - 06 } ; wins } ; hop { filter_eq { all_rows ; season ; 2006 - 07 } ; wins } } = true', 'tointer': 'select the rows whose season record fuzzily matches to 2005 - 06 . take the wins record of this row . select the rows whose season record fuzzily matches to 2006 - 07 . take the wins record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; season ; 2005 - 06 } ; wins } ; hop { filter_eq { all_rows ; season ; 2006 - 07 } ; wins } } = true | select the rows whose season record fuzzily matches to 2005 - 06 . take the wins record of this row . select the rows whose season record fuzzily matches to 2006 - 07 . take the wins 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, 'season_7': 7, '2005 - 06_8': 8, 'wins_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'season_11': 11, '2006 - 07_12': 12, 'wins_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', 'season_7': 'season', '2005 - 06_8': '2005 - 06', 'wins_9': 'wins', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'season_11': 'season', '2006 - 07_12': '2006 - 07', 'wins_13': 'wins'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'season_7': [0], '2005 - 06_8': [0], 'wins_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'season_11': [1], '2006 - 07_12': [1], 'wins_13': [3]} | ['season', 'league', 'finish', 'wins', 'losses', 'pct'] | [['arkansas rimrockers', 'arkansas rimrockers', 'arkansas rimrockers', 'arkansas rimrockers', 'arkansas rimrockers', 'arkansas rimrockers'], ['2004 - 05', 'aba', '1st', '28', '5', '848'], ['2005 - 06', 'd - league', '5th', '24', '24', '500'], ['2006 - 07', 'd - league', '6th', '16', '34', '320'], ['regular season', 'regular season', 'regular season', '68', '63', '519'], ['playoffs', 'playoffs', 'playoffs', '4', '0', '1.000']] |
1922 u.s. open ( golf ) | https://en.wikipedia.org/wiki/1922_U.S._Open_%28golf%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18007045-1.html.csv | count | in the 1922 u.s. open , two of the players were from scotland . | {'scope': 'all', 'criterion': 'equal', 'value': 'scotland', 'result': '2', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'scotland'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to scotland .', 'tostr': 'filter_eq { all_rows ; country ; scotland }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; country ; scotland } }', 'tointer': 'select the rows whose country record fuzzily matches to scotland . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; country ; scotland } } ; 2 } = true', 'tointer': 'select the rows whose country record fuzzily matches to scotland . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; country ; scotland } } ; 2 } = true | select the rows whose country record fuzzily matches to scotland . 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, 'country_5': 5, 'scotland_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', 'country_5': 'country', 'scotland_6': 'scotland', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'country_5': [0], 'scotland_6': [0], '2_7': [2]} | ['place', 'player', 'country', 'score', 'to par', 'money'] | [['1', 'gene sarazen', 'united states', '72 + 73 + 75 + 68 = 288', '+ 8', '500'], ['t2', 'john black', 'scotland', '71 + 71 + 75 + 72 = 289', '+ 9', '300'], ['t2', 'bobby jones ( a )', 'united states', '74 + 72 + 70 + 73 = 289', '+ 9', '0'], ['4', 'bill mehlhorn', 'united states', '73 + 71 + 72 + 74 = 290', '+ 10', '200'], ['5', 'walter hagen', 'united states', '68 + 77 + 74 + 72 = 291', '+ 11', '150'], ['6', 'george duncan', 'scotland', '76 + 73 + 75 + 72 = 296', '+ 16', '100'], ['7', 'leo diegel', 'united states', '77 + 76 + 73 + 71 = 297', '+ 17', '90'], ['t8', 'mike brady', 'united states', '73 + 75 + 74 + 76 = 298', '+ 18', '73'], ['t8', 'johnny golden', 'united states', '73 + 77 + 77 + 71 = 298', '+ 18', '73'], ['t8', 'jock hutchison', 'united states', '78 + 74 + 71 + 75 = 298', '+ 18', '73']] |
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