niryuu/sni-each-converted huggingface dataset:
Command: eq { hop { filter_eq { all_rows ; competition ; 1978 merdeka cup } ; score } ; hop { filter_eq { all_rows ; competition ; 1986 fifa world cup } ; score } }, interpretation: select the rows whose competition record fuzzily matches to 1978 merdeka cup . take the score record of this row . select the rows whose competition record fuzzily matches to 1986 fifa world cup . take the score record of this row . the first record fuzzily matches to the second record .
Output: yes
Command: eq { hop { filter_eq { all_rows ; competition ; 1978 merdeka cup } ; score } ; hop { filter_eq { all_rows ; competition ; 1986 fifa world cup } ; score } }, interpretation: select the rows whose opponent record fuzzily matches to cleveland browns . take the attendance record of this row . select the rows whose opponent record fuzzily matches to dallas cowboys . take the attendance record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; opponent ; cleveland browns } ; attendance } ; hop { filter_eq { all_rows ; opponent ; dallas cowboys } ; attendance } }, interpretation: select the rows whose opponent record fuzzily matches to cleveland browns . take the attendance record of this row . select the rows whose opponent record fuzzily matches to dallas cowboys . take the attendance record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; opponent ; cleveland browns } ; attendance } ; hop { filter_eq { all_rows ; opponent ; dallas cowboys } ; attendance } }, interpretation: select the rows whose segment a record fuzzily matches to three wheeled vehicles . take the episode record of this row . select the rows whose segment a record fuzzily matches to air filters . take the episode record of this row . the first record is less than the second record . the episode record of the first row is 66 . the episode record of the second row is 70 .
Output: no
Command: and { less { hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode } ; hop { filter_eq { all_rows ; segment a ; air filters } ; episode } } ; and { eq { hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode } ; 66 } ; eq { hop { filter_eq { all_rows ; segment a ; air filters } ; episode } ; 70 } } }, interpretation: select the rows whose segment a record fuzzily matches to three wheeled vehicles . take the episode record of this row . select the rows whose segment a record fuzzily matches to air filters . take the episode record of this row . the first record is less than the second record . the episode record of the first row is 66 . the episode record of the second row is 70 .
Output: yes
Command: and { less { hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode } ; hop { filter_eq { all_rows ; segment a ; air filters } ; episode } } ; and { eq { hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode } ; 66 } ; eq { hop { filter_eq { all_rows ; segment a ; air filters } ; episode } ; 70 } } }, interpretation: select the rows whose top - 5 record is equal to 0 . there is only one such row in the table . the tournament record of this unqiue row is pga championship .
Output: no
Command: and { only { filter_eq { all_rows ; top - 5 ; 0 } } ; eq { hop { filter_eq { all_rows ; top - 5 ; 0 } ; tournament } ; pga championship } }, interpretation: select the rows whose top - 5 record is equal to 0 . there is only one such row in the table . the tournament record of this unqiue row is pga championship .
Output: yes
Command: and { only { filter_eq { all_rows ; top - 5 ; 0 } } ; eq { hop { filter_eq { all_rows ; top - 5 ; 0 } ; tournament } ; pga championship } }, interpretation: select the rows whose position record fuzzily matches to point guard . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; position ; point guard } } ; 2 }, interpretation: select the rows whose position record fuzzily matches to point guard . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; position ; point guard } } ; 2 }, interpretation: the average of the international tourist arrivals ( 2012 ) record of all rows is 13.4 million .
Output: no
Command: round_eq { avg { all_rows ; international tourist arrivals ( 2012 ) } ; 13.4 million }, interpretation: the average of the international tourist arrivals ( 2012 ) record of all rows is 13.4 million .
Output: yes
Command: round_eq { avg { all_rows ; international tourist arrivals ( 2012 ) } ; 13.4 million }, interpretation: the average of the total receipts record of all rows is 12444351 .
Output: no
Command: round_eq { avg { all_rows ; total receipts } ; 12444351 }, interpretation: the average of the total receipts record of all rows is 12444351 .
Output: yes
Command: round_eq { avg { all_rows ; total receipts } ; 12444351 }, interpretation: select the rows whose year record is less than or equal to 2002 . the average of the winnings record of these rows is 44881 .
Output: no
Command: round_eq { avg { filter_less_eq { all_rows ; year ; 2002 } ; winnings } ; 44881 }, interpretation: select the rows whose year record is less than or equal to 2002 . the average of the winnings record of these rows is 44881 .
Output: yes
Command: round_eq { avg { filter_less_eq { all_rows ; year ; 2002 } ; winnings } ; 44881 }, interpretation: select the rows whose royal house record fuzzily matches to jiang . there is only one such row in the table . the state record of this unqiue row is qi .
Output: no
Command: and { only { filter_eq { all_rows ; royal house ; jiang } } ; eq { hop { filter_eq { all_rows ; royal house ; jiang } ; state } ; qi } }, interpretation: select the rows whose royal house record fuzzily matches to jiang . there is only one such row in the table . the state record of this unqiue row is qi .
Output: yes
Command: and { only { filter_eq { all_rows ; royal house ; jiang } } ; eq { hop { filter_eq { all_rows ; royal house ; jiang } ; state } ; qi } }, interpretation: select the rows whose homeport ( as of july 2013 ) record fuzzily matches to portsmouth . the number of such rows is 6 .
Output: no
Command: eq { count { filter_eq { all_rows ; homeport ( as of july 2013 ) ; portsmouth } } ; 6 }, interpretation: select the rows whose homeport ( as of july 2013 ) record fuzzily matches to portsmouth . the number of such rows is 6 .
Output: yes
Command: eq { count { filter_eq { all_rows ; homeport ( as of july 2013 ) ; portsmouth } } ; 6 }, interpretation: select the row whose total record of all rows is 1st maximum . the player record of this row is lee janzen .
Output: no
Command: eq { hop { nth_argmax { all_rows ; total ; 1 } ; player } ; lee janzen }, interpretation: select the row whose total record of all rows is 1st maximum . the player record of this row is lee janzen .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; total ; 1 } ; player } ; lee janzen }, interpretation: for the party records of all rows , most of them fuzzily match to democratic .
Output: no
Command: most_eq { all_rows ; party ; democratic }, interpretation: for the party records of all rows , most of them fuzzily match to democratic .
Output: yes
Command: most_eq { all_rows ; party ; democratic }, interpretation: select the row whose date record of all rows is 2nd maximum . the opponent record of this row is maša zec peškirič .
Output: no
Command: eq { hop { nth_argmax { all_rows ; date ; 2 } ; opponent } ; maša zec peškirič }, interpretation: select the row whose date record of all rows is 2nd maximum . the opponent record of this row is maša zec peškirič .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; date ; 2 } ; opponent } ; maša zec peškirič }, interpretation: select the row whose result record of all rows is minimum . the driver record of this row is emerson fittipaldi .
Output: no
Command: eq { hop { argmin { all_rows ; result } ; driver } ; emerson fittipaldi }, interpretation: select the row whose result record of all rows is minimum . the driver record of this row is emerson fittipaldi .
Output: yes
Command: eq { hop { argmin { all_rows ; result } ; driver } ; emerson fittipaldi }, interpretation: for the location records of all rows , most of them fuzzily match to edison international field of anaheim .
Output: no
Command: most_eq { all_rows ; location ; edison international field of anaheim }, interpretation: for the location records of all rows , most of them fuzzily match to edison international field of anaheim .
Output: yes
Command: most_eq { all_rows ; location ; edison international field of anaheim }, interpretation: select the rows whose satellite record fuzzily matches to echo 2 . take the mass ( kg ) record of this row . select the rows whose satellite record fuzzily matches to mylar balloon . take the mass ( kg ) record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; satellite ; echo 2 } ; mass ( kg ) } ; hop { filter_eq { all_rows ; satellite ; mylar balloon } ; mass ( kg ) } }, interpretation: select the rows whose satellite record fuzzily matches to echo 2 . take the mass ( kg ) record of this row . select the rows whose satellite record fuzzily matches to mylar balloon . take the mass ( kg ) record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; satellite ; echo 2 } ; mass ( kg ) } ; hop { filter_eq { all_rows ; satellite ; mylar balloon } ; mass ( kg ) } }, interpretation: select the rows whose reason for non - seating record fuzzily matches to elected , but declined to take office . there is only one such row in the table . the member - elect record of this unqiue row is john cantine .
Output: no
Command: and { only { filter_eq { all_rows ; reason for non - seating ; elected , but declined to take office } } ; eq { hop { filter_eq { all_rows ; reason for non - seating ; elected , but declined to take office } ; member - elect } ; john cantine } }, interpretation: select the rows whose reason for non - seating record fuzzily matches to elected , but declined to take office . there is only one such row in the table . the member - elect record of this unqiue row is john cantine .
Output: yes
Command: and { only { filter_eq { all_rows ; reason for non - seating ; elected , but declined to take office } } ; eq { hop { filter_eq { all_rows ; reason for non - seating ; elected , but declined to take office } ; member - elect } ; john cantine } }, interpretation: select the rows whose rider record fuzzily matches to suzuki . there is only one such row in the table .
Output: no
Command: only { filter_eq { all_rows ; rider ; suzuki } }, interpretation: select the rows whose rider record fuzzily matches to suzuki . there is only one such row in the table .
Output: yes
Command: only { filter_eq { all_rows ; rider ; suzuki } }, interpretation: select the rows whose date record fuzzily matches to october . for the result records of these rows , most of them fuzzily match to w .
Output: no
Command: most_eq { filter_eq { all_rows ; date ; october } ; result ; w }, interpretation: select the rows whose date record fuzzily matches to october . for the result records of these rows , most of them fuzzily match to w .
Output: yes
Command: most_eq { filter_eq { all_rows ; date ; october } ; result ; w }, interpretation: for the overall wc points ( rank ) records of all rows , most of them are less than 500 .
Output: no
Command: most_less { all_rows ; overall wc points ( rank ) ; 500 }, interpretation: for the overall wc points ( rank ) records of all rows , most of them are less than 500 .
Output: yes
Command: most_less { all_rows ; overall wc points ( rank ) ; 500 }, interpretation: the average of the year erected record of all rows is 1965 .
Output: no
Command: round_eq { avg { all_rows ; year erected } ; 1965 }, interpretation: the average of the year erected record of all rows is 1965 .
Output: yes
Command: round_eq { avg { all_rows ; year erected } ; 1965 }, interpretation: for the first elected records of all rows , most of them are greater than 1960 .
Output: no
Command: most_greater { all_rows ; first elected ; 1960 }, interpretation: for the first elected records of all rows , most of them are greater than 1960 .
Output: yes
Command: most_greater { all_rows ; first elected ; 1960 }, interpretation: the average of the erp w record of all rows is 37.5 watts .
Output: no
Command: round_eq { avg { all_rows ; erp w } ; 37.5 watts }, interpretation: the average of the erp w record of all rows is 37.5 watts .
Output: yes
Command: round_eq { avg { all_rows ; erp w } ; 37.5 watts }, interpretation: select the rows whose metropolitan area record fuzzily matches to jakarta . take the population record of this row . select the rows whose metropolitan area record fuzzily matches to beijing . take the population record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; metropolitan area ; jakarta } ; population } ; hop { filter_eq { all_rows ; metropolitan area ; beijing } ; population } }, interpretation: select the rows whose metropolitan area record fuzzily matches to jakarta . take the population record of this row . select the rows whose metropolitan area record fuzzily matches to beijing . take the population record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; metropolitan area ; jakarta } ; population } ; hop { filter_eq { all_rows ; metropolitan area ; beijing } ; population } }, interpretation: select the rows whose opponent record fuzzily matches to ramazan ramazanov . take the round record of this row . select the rows whose opponent record fuzzily matches to eduardo maiorino . take the round record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; opponent ; ramazan ramazanov } ; round } ; hop { filter_eq { all_rows ; opponent ; eduardo maiorino } ; round } }, interpretation: select the rows whose opponent record fuzzily matches to ramazan ramazanov . take the round record of this row . select the rows whose opponent record fuzzily matches to eduardo maiorino . take the round record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; opponent ; ramazan ramazanov } ; round } ; hop { filter_eq { all_rows ; opponent ; eduardo maiorino } ; round } }, interpretation: the average of the points record of all rows is 30 .
Output: no
Command: round_eq { avg { all_rows ; points } ; 30 }, interpretation: the average of the points record of all rows is 30 .
Output: yes
Command: round_eq { avg { all_rows ; points } ; 30 }, interpretation: select the rows whose college record fuzzily matches to north carolina . select the row whose height record of these rows is maximum . the player record of this row is jeff lebo .
Output: no
Command: eq { hop { argmax { filter_eq { all_rows ; college ; north carolina } ; height } ; player } ; jeff lebo }, interpretation: select the rows whose college record fuzzily matches to north carolina . select the row whose height record of these rows is maximum . the player record of this row is jeff lebo .
Output: yes
Command: eq { hop { argmax { filter_eq { all_rows ; college ; north carolina } ; height } ; player } ; jeff lebo }, interpretation: select the row whose asts record of all rows is maximum . the player record of this row is gail goodrich .
Output: no
Command: eq { hop { argmax { all_rows ; asts } ; player } ; gail goodrich }, interpretation: select the row whose asts record of all rows is maximum . the player record of this row is gail goodrich .
Output: yes
Command: eq { hop { argmax { all_rows ; asts } ; player } ; gail goodrich }, interpretation: select the rows whose wheels record is equal to 5 . there is only one such row in the table . the class record of this unqiue row is 0 - 6 - 0t .
Output: no
Command: and { only { filter_eq { all_rows ; wheels ; 5 } } ; eq { hop { filter_eq { all_rows ; wheels ; 5 } ; class } ; 0 - 6 - 0t } }, interpretation: select the rows whose wheels record is equal to 5 . there is only one such row in the table . the class record of this unqiue row is 0 - 6 - 0t .
Output: yes
Command: and { only { filter_eq { all_rows ; wheels ; 5 } } ; eq { hop { filter_eq { all_rows ; wheels ; 5 } ; class } ; 0 - 6 - 0t } }, interpretation: select the row whose no record of all rows is 3rd maximum . the player record of this row is ashraf amaya .
Output: no
Command: eq { hop { nth_argmax { all_rows ; no ; 3 } ; player } ; ashraf amaya }, interpretation: select the row whose no record of all rows is 3rd maximum . the player record of this row is ashraf amaya .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; no ; 3 } ; player } ; ashraf amaya }, interpretation: select the row whose goals scored record of all rows is maximum . the club record of this row is fbk kaunas .
Output: no
Command: eq { hop { argmax { all_rows ; goals scored } ; club } ; fbk kaunas }, interpretation: select the row whose goals scored record of all rows is maximum . the club record of this row is fbk kaunas .
Output: yes
Command: eq { hop { argmax { all_rows ; goals scored } ; club } ; fbk kaunas }, interpretation: for the label records of all rows , all of them fuzzily match to linfair records .
Output: no
Command: all_eq { all_rows ; label ; linfair records }, interpretation: for the label records of all rows , all of them fuzzily match to linfair records .
Output: yes
Command: all_eq { all_rows ; label ; linfair records }, interpretation: select the rows whose team record fuzzily matches to sandviken . take the in toppserien since record of this row . select the rows whose team record fuzzily matches to kolbotn . take the in toppserien since record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; team ; sandviken } ; in toppserien since } ; hop { filter_eq { all_rows ; team ; kolbotn } ; in toppserien since } }, interpretation: select the rows whose team record fuzzily matches to sandviken . take the in toppserien since record of this row . select the rows whose team record fuzzily matches to kolbotn . take the in toppserien since record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; team ; sandviken } ; in toppserien since } ; hop { filter_eq { all_rows ; team ; kolbotn } ; in toppserien since } }, interpretation: select the rows whose type record fuzzily matches to public . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; type ; public } } ; 3 }, interpretation: select the rows whose type record fuzzily matches to public . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; type ; public } } ; 3 }, interpretation: select the rows whose official name record fuzzily matches to hillsborough . take the population record of this row . select the rows whose official name record fuzzily matches to hopewell . take the population record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; official name ; hillsborough } ; population } ; hop { filter_eq { all_rows ; official name ; hopewell } ; population } }, interpretation: select the rows whose official name record fuzzily matches to hillsborough . take the population record of this row . select the rows whose official name record fuzzily matches to hopewell . take the population record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; official name ; hillsborough } ; population } ; hop { filter_eq { all_rows ; official name ; hopewell } ; population } }, interpretation: select the row whose attendance record of all rows is maximum . the date record of this row is november 3 , 1968 .
Output: no
Command: eq { hop { argmax { all_rows ; attendance } ; date } ; november 3 , 1968 }, interpretation: select the row whose attendance record of all rows is maximum . the date record of this row is november 3 , 1968 .
Output: yes
Command: eq { hop { argmax { all_rows ; attendance } ; date } ; november 3 , 1968 }, interpretation: for the first elected records of all rows , most of them are less than 1920 .
Output: no
Command: most_less { all_rows ; first elected ; 1920 }, interpretation: for the first elected records of all rows , most of them are less than 1920 .
Output: yes
Command: most_less { all_rows ; first elected ; 1920 }, interpretation: the average of the money raised , 2q record of all rows is 10,369,822.5 .
Output: no
Command: round_eq { avg { all_rows ; money raised , 2q } ; 10,369,822.5 }, interpretation: the average of the money raised , 2q record of all rows is 10,369,822.5 .
Output: yes
Command: round_eq { avg { all_rows ; money raised , 2q } ; 10,369,822.5 }, interpretation: select the rows whose club record fuzzily matches to rcd español . take the wins record of this row . select the rows whose club record fuzzily matches to pontevedra cf . take the wins record of this row . the first record is 1 larger than the second record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; club ; rcd español } ; wins } ; hop { filter_eq { all_rows ; club ; pontevedra cf } ; wins } } ; 1 }, interpretation: select the rows whose club record fuzzily matches to rcd español . take the wins record of this row . select the rows whose club record fuzzily matches to pontevedra cf . take the wins record of this row . the first record is 1 larger than the second record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; club ; rcd español } ; wins } ; hop { filter_eq { all_rows ; club ; pontevedra cf } ; wins } } ; 1 }, interpretation: for the prothrombin time records of all rows , most of them fuzzily match to unaffected .
Output: no
Command: most_eq { all_rows ; prothrombin time ; unaffected }, interpretation: for the prothrombin time records of all rows , most of them fuzzily match to unaffected .
Output: yes
Command: most_eq { all_rows ; prothrombin time ; unaffected }, interpretation: the average of the weight ( kg ) record of all rows is 54.29 .
Output: no
Command: round_eq { avg { all_rows ; weight ( kg ) } ; 54.29 }, interpretation: the average of the weight ( kg ) record of all rows is 54.29 .
Output: yes
Command: round_eq { avg { all_rows ; weight ( kg ) } ; 54.29 }, interpretation: select the rows whose game record is arbitrary . the number of such rows is 10 .
Output: no
Command: eq { count { filter_all { all_rows ; game } } ; 10 }, interpretation: select the rows whose game record is arbitrary . the number of such rows is 10 .
Output: yes
Command: eq { count { filter_all { all_rows ; game } } ; 10 }, interpretation: select the row whose margin of victory record of all rows is maximum . the tournament record of this row is aberdeen ladies scottish open .
Output: no
Command: eq { hop { argmax { all_rows ; margin of victory } ; tournament } ; aberdeen ladies scottish open }, interpretation: select the row whose margin of victory record of all rows is maximum . the tournament record of this row is aberdeen ladies scottish open .
Output: yes
Command: eq { hop { argmax { all_rows ; margin of victory } ; tournament } ; aberdeen ladies scottish open }, interpretation: select the row whose grid record of all rows is maximum . the driver record of this row is zsolt baumgartner .
Output: no
Command: eq { hop { argmax { all_rows ; grid } ; driver } ; zsolt baumgartner }, interpretation: select the row whose grid record of all rows is maximum . the driver record of this row is zsolt baumgartner .
Output: yes
Command: eq { hop { argmax { all_rows ; grid } ; driver } ; zsolt baumgartner }, interpretation: select the rows whose name record fuzzily matches to telmatosaurus . take the location record of this row . select the rows whose name record fuzzily matches to brachiosaurus . take the location record of this row . the first record does not match to the second record . the location record of the first row is romania . the location record of the second row is usa .
Output: no
Command: and { not_eq { hop { filter_eq { all_rows ; name ; telmatosaurus } ; location } ; hop { filter_eq { all_rows ; name ; brachiosaurus } ; location } } ; and { eq { hop { filter_eq { all_rows ; name ; telmatosaurus } ; location } ; romania } ; eq { hop { filter_eq { all_rows ; name ; brachiosaurus } ; location } ; usa } } }, interpretation: select the rows whose name record fuzzily matches to telmatosaurus . take the location record of this row . select the rows whose name record fuzzily matches to brachiosaurus . take the location record of this row . the first record does not match to the second record . the location record of the first row is romania . the location record of the second row is usa .
Output: yes
Command: and { not_eq { hop { filter_eq { all_rows ; name ; telmatosaurus } ; location } ; hop { filter_eq { all_rows ; name ; brachiosaurus } ; location } } ; and { eq { hop { filter_eq { all_rows ; name ; telmatosaurus } ; location } ; romania } ; eq { hop { filter_eq { all_rows ; name ; brachiosaurus } ; location } ; usa } } }, interpretation: select the row whose founded record of all rows is 1st minimum . the institution record of this row is guilford college .
Output: no
Command: eq { hop { nth_argmin { all_rows ; founded ; 1 } ; institution } ; guilford college }, interpretation: select the row whose founded record of all rows is 1st minimum . the institution record of this row is guilford college .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; founded ; 1 } ; institution } ; guilford college }, interpretation: for the party records of all rows , all of them fuzzily match to democratic .
Output: no
Command: all_eq { all_rows ; party ; democratic }, interpretation: for the party records of all rows , all of them fuzzily match to democratic .
Output: yes
Command: all_eq { all_rows ; party ; democratic }, interpretation: select the rows whose density record is equal to 33 . there is only one such row in the table . the english name record of this unqiue row is luobei county .
Output: no
Command: and { only { filter_eq { all_rows ; density ; 33 } } ; eq { hop { filter_eq { all_rows ; density ; 33 } ; english name } ; luobei county } }, interpretation: select the rows whose density record is equal to 33 . there is only one such row in the table . the english name record of this unqiue row is luobei county .
Output: yes
Command: and { only { filter_eq { all_rows ; density ; 33 } } ; eq { hop { filter_eq { all_rows ; density ; 33 } ; english name } ; luobei county } }, interpretation: the average of the won record of all rows is 26 .
Output: no
Command: round_eq { avg { all_rows ; won } ; 26 }, interpretation: the average of the won record of all rows is 26 .
Output: yes
Command: round_eq { avg { all_rows ; won } ; 26 }, interpretation: for the score records of all rows , most of them are equal to 68 .
Output: no
Command: most_eq { all_rows ; score ; 68 }, interpretation: for the score records of all rows , most of them are equal to 68 .
Output: yes
Command: most_eq { all_rows ; score ; 68 }, interpretation: for the series records of all rows , most of them fuzzily match to lt .
Output: no
Command: most_eq { all_rows ; series ; lt }, interpretation: for the series records of all rows , most of them fuzzily match to lt .
Output: yes
Command: most_eq { all_rows ; series ; lt }, interpretation: select the row whose away record of all rows is 2nd maximum . the opponent record of this row is slovan bratislava .
Output: no
Command: eq { hop { nth_argmax { all_rows ; away ; 2 } ; opponent } ; slovan bratislava }, interpretation: select the row whose away record of all rows is 2nd maximum . the opponent record of this row is slovan bratislava .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; away ; 2 } ; opponent } ; slovan bratislava }, interpretation: select the rows whose nationality record fuzzily matches to united states . among these rows , select the rows whose college / junior / club team ( league ) record fuzzily matches to university of notre dame . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; nationality ; united states } ; college / junior / club team ( league ) ; university of notre dame } } ; 2 }, interpretation: select the rows whose nationality record fuzzily matches to united states . among these rows , select the rows whose college / junior / club team ( league ) record fuzzily matches to university of notre dame . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; nationality ; united states } ; college / junior / club team ( league ) ; university of notre dame } } ; 2 }, interpretation: select the rows whose texas vs record fuzzily matches to missouri . take the current streak record of this row . select the rows whose texas vs record fuzzily matches to oklahoma . take the current streak record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; texas vs ; missouri } ; current streak } ; hop { filter_eq { all_rows ; texas vs ; oklahoma } ; current streak } }, interpretation: select the rows whose texas vs record fuzzily matches to missouri . take the current streak record of this row . select the rows whose texas vs record fuzzily matches to oklahoma . take the current streak record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; texas vs ; missouri } ; current streak } ; hop { filter_eq { all_rows ; texas vs ; oklahoma } ; current streak } }, interpretation: select the rows whose original air date record fuzzily matches to april . there is only one such row in the table . the title record of this unqiue row is the hamptons .
Output: no
Command: and { only { filter_eq { all_rows ; original air date ; april } } ; eq { hop { filter_eq { all_rows ; original air date ; april } ; title } ; the hamptons } }, interpretation: select the rows whose original air date record fuzzily matches to april . there is only one such row in the table . the title record of this unqiue row is the hamptons .
Output: yes
Command: and { only { filter_eq { all_rows ; original air date ; april } } ; eq { hop { filter_eq { all_rows ; original air date ; april } ; title } ; the hamptons } }, interpretation: for the played records of all rows , most of them are equal to 38 .
Output: no
Command: most_eq { all_rows ; played ; 38 }, interpretation: for the played records of all rows , most of them are equal to 38 .
Output: yes
Command: most_eq { all_rows ; played ; 38 }, interpretation: select the rows whose venue record fuzzily matches to away . select the row whose attendance record of these rows is maximum . the opponent record of this row is luton town .
Output: no
Command: eq { hop { argmax { filter_eq { all_rows ; venue ; away } ; attendance } ; opponent } ; luton town }, interpretation: select the rows whose venue record fuzzily matches to away . select the row whose attendance record of these rows is maximum . the opponent record of this row is luton town .
Output: yes
Command: eq { hop { argmax { filter_eq { all_rows ; venue ; away } ; attendance } ; opponent } ; luton town }, interpretation: select the rows whose rank record fuzzily matches to 2 . take the silver record of this row . select the rows whose rank record fuzzily matches to 1 . take the silver record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; rank ; 2 } ; silver } ; hop { filter_eq { all_rows ; rank ; 1 } ; silver } }, interpretation: select the rows whose rank record fuzzily matches to 2 . take the silver record of this row . select the rows whose rank record fuzzily matches to 1 . take the silver record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; rank ; 2 } ; silver } ; hop { filter_eq { all_rows ; rank ; 1 } ; silver } }, interpretation: select the rows whose date record fuzzily matches to 13 may 1998 . take the weight ( kg ) record of this row . select the rows whose date record fuzzily matches to 23 aug 1997 . take the weight ( kg ) record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; date ; 13 may 1998 } ; weight ( kg ) } ; hop { filter_eq { all_rows ; date ; 23 aug 1997 } ; weight ( kg ) } }, interpretation: select the rows whose date record fuzzily matches to 13 may 1998 . take the weight ( kg ) record of this row . select the rows whose date record fuzzily matches to 23 aug 1997 . take the weight ( kg ) record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; date ; 13 may 1998 } ; weight ( kg ) } ; hop { filter_eq { all_rows ; date ; 23 aug 1997 } ; weight ( kg ) } }, interpretation: select the row whose attendance record of all rows is maximum . the date record of this row is november 14 .
Output: no
Command: eq { hop { argmax { all_rows ; attendance } ; date } ; november 14 }, interpretation: select the row whose attendance record of all rows is maximum . the date record of this row is november 14 .
Output: yes
Command: eq { hop { argmax { all_rows ; attendance } ; date } ; november 14 }, interpretation: for the surface records of all rows , all of them fuzzily match to clay .
Output: no
Command: all_eq { all_rows ; surface ; clay }, interpretation: for the surface records of all rows , all of them fuzzily match to clay .
Output: yes
Command: all_eq { all_rows ; surface ; clay }, interpretation: select the rows whose yards record is equal to 39 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; yards ; 39 } } ; 2 }, interpretation: select the rows whose yards record is equal to 39 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; yards ; 39 } } ; 2 }, interpretation: select the row whose year joined record of all rows is maximum . the school ( ihsaa id ) record of this row is bremen .
Output: no
Command: eq { hop { argmax { all_rows ; year joined } ; school ( ihsaa id ) } ; bremen }, interpretation: select the row whose year joined record of all rows is maximum . the school ( ihsaa id ) record of this row is bremen .
Output: yes
Command: eq { hop { argmax { all_rows ; year joined } ; school ( ihsaa id ) } ; bremen }, interpretation: the sum of the laps record of all rows is 810 .
Output: no
Command: round_eq { sum { all_rows ; laps } ; 810 }, interpretation: the sum of the laps record of all rows is 810 .
Output: yes
Command: round_eq { sum { all_rows ; laps } ; 810 }, interpretation: the maximum weight record of all rows is 247 . the name record of the row with superlative weight record is matt kleinmann .
Output: no
Command: and { eq { max { all_rows ; weight } ; 247 } ; eq { hop { argmax { all_rows ; weight } ; name } ; matt kleinmann } }, interpretation: the maximum weight record of all rows is 247 . the name record of the row with superlative weight record is matt kleinmann .
Output: yes
Command: and { eq { max { all_rows ; weight } ; 247 } ; eq { hop { argmax { all_rows ; weight } ; name } ; matt kleinmann } }, interpretation: select the rows whose deaths record fuzzily matches to none . there is only one such row in the table . the year record of this unqiue row is 1864 .
Output: no
Command: and { only { filter_eq { all_rows ; deaths ; none } } ; eq { hop { filter_eq { all_rows ; deaths ; none } ; year } ; 1864 } }, interpretation: select the rows whose deaths record fuzzily matches to none . there is only one such row in the table . the year record of this unqiue row is 1864 .
Output: yes
Command: and { only { filter_eq { all_rows ; deaths ; none } } ; eq { hop { filter_eq { all_rows ; deaths ; none } ; year } ; 1864 } }, interpretation: for the laps records of all rows , most of them are equal to 30 .
Output: no
Command: most_eq { all_rows ; laps ; 30 }, interpretation: for the laps records of all rows , most of them are equal to 30 .
Output: yes
Command: most_eq { all_rows ; laps ; 30 }, interpretation: select the rows whose nation record is arbitrary . the number of such rows is 5 .
Output: no
Command: eq { count { filter_all { all_rows ; nation } } ; 5 }, interpretation: select the rows whose nation record is arbitrary . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_all { all_rows ; nation } } ; 5 }, interpretation: select the rows whose incumbent record fuzzily matches to gene taylor . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to harold volkmer . take the first elected record of this row . the second record is 4 years larger than the first record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; incumbent ; gene taylor } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; harold volkmer } ; first elected } } ; -4 years }, interpretation: select the rows whose incumbent record fuzzily matches to gene taylor . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to harold volkmer . take the first elected record of this row . the second record is 4 years larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; incumbent ; gene taylor } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; harold volkmer } ; first elected } } ; -4 years }, interpretation: the sum of the prize ( k ) record of all rows is 1920 .
Output: no
Command: round_eq { sum { all_rows ; prize ( k ) } ; 1920 }, interpretation: the sum of the prize ( k ) record of all rows is 1920 .
Output: yes
Command: round_eq { sum { all_rows ; prize ( k ) } ; 1920 }, interpretation: the sum of the us viewers ( millions ) record of all rows is 78.52 .
Output: no
Command: round_eq { sum { all_rows ; us viewers ( millions ) } ; 78.52 }, interpretation: the sum of the us viewers ( millions ) record of all rows is 78.52 .
Output: yes
Command: round_eq { sum { all_rows ; us viewers ( millions ) } ; 78.52 }, interpretation: select the row whose points record of all rows is 1st maximum . the name record of this row is jakim donaldson .
Output: no
Command: eq { hop { nth_argmax { all_rows ; points ; 1 } ; name } ; jakim donaldson }, interpretation: select the row whose points record of all rows is 1st maximum . the name record of this row is jakim donaldson .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; points ; 1 } ; name } ; jakim donaldson }, interpretation: select the rows whose time of day record fuzzily matches to afternoon . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; time of day ; afternoon } } ; 3 }, interpretation: select the rows whose time of day record fuzzily matches to afternoon . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; time of day ; afternoon } } ; 3 }, interpretation: for the high points records of all rows , most of them fuzzily match to j johnson .
Output: no
Command: most_eq { all_rows ; high points ; j johnson }, interpretation: for the high points records of all rows , most of them fuzzily match to j johnson .
Output: yes
Command: most_eq { all_rows ; high points ; j johnson }, interpretation: for the founded records of all rows , most of them are less than 1900 .
Output: no
Command: most_less { all_rows ; founded ; 1900 }, interpretation: for the founded records of all rows , most of them are less than 1900 .
Output: yes
Command: most_less { all_rows ; founded ; 1900 }, interpretation: the maximum score record of all rows is 5 - 1 .
Output: no
Command: eq { max { all_rows ; score } ; 5 - 1 }, interpretation: the maximum score record of all rows is 5 - 1 .
Output: yes
Command: eq { max { all_rows ; score } ; 5 - 1 }, interpretation: select the rows whose current streak record fuzzily matches to l . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; current streak ; l } } ; 2 }, interpretation: select the rows whose current streak record fuzzily matches to l . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; current streak ; l } } ; 2 }, interpretation: select the row whose total record of all rows is maximum . the nation record of this row is soviet union .
Output: no
Command: eq { hop { argmax { all_rows ; total } ; nation } ; soviet union }, interpretation: select the row whose total record of all rows is maximum . the nation record of this row is soviet union .
Output: yes
Command: eq { hop { argmax { all_rows ; total } ; nation } ; soviet union }, interpretation: select the rows whose tournament record fuzzily matches to singapore international . there is only one such row in the table . the year record of this unqiue row is 1999 .
Output: no
Command: and { only { filter_eq { all_rows ; tournament ; singapore international } } ; eq { hop { filter_eq { all_rows ; tournament ; singapore international } ; year } ; 1999 } }, interpretation: select the rows whose tournament record fuzzily matches to singapore international . there is only one such row in the table . the year record of this unqiue row is 1999 .
Output: yes
Command: and { only { filter_eq { all_rows ; tournament ; singapore international } } ; eq { hop { filter_eq { all_rows ; tournament ; singapore international } ; year } ; 1999 } }, interpretation: select the row whose no record of all rows is 2nd minimum . the player record of this row is derrick favors .
Output: no
Command: eq { hop { nth_argmin { all_rows ; no ; 2 } ; player } ; derrick favors }, interpretation: select the row whose no record of all rows is 2nd minimum . the player record of this row is derrick favors .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; no ; 2 } ; player } ; derrick favors }, interpretation: select the row whose age record of all rows is 7th minimum . the name record of this row is konstantopoulos .
Output: no
Command: eq { hop { nth_argmin { all_rows ; age ; 7 } ; name } ; konstantopoulos }, interpretation: select the row whose age record of all rows is 7th minimum . the name record of this row is konstantopoulos .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; age ; 7 } ; name } ; konstantopoulos }, interpretation: for the method records of all rows , most of them fuzzily match to tko .
Output: no
Command: most_eq { all_rows ; method ; tko }, interpretation: for the method records of all rows , most of them fuzzily match to tko .
Output: yes
Command: most_eq { all_rows ; method ; tko }, interpretation: select the rows whose winner record fuzzily matches to bobby clarke . among these rows , select the rows whose goals record is equal to 51 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; winner ; bobby clarke } ; goals ; 51 } } ; 2 }, interpretation: select the rows whose winner record fuzzily matches to bobby clarke . among these rows , select the rows whose goals record is equal to 51 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; winner ; bobby clarke } ; goals ; 51 } } ; 2 }, interpretation: select the rows whose name of ground record fuzzily matches to chester road north ground . take the worcs la matches record of this row . select the rows whose name of ground record fuzzily matches to racecourse ground . take the worcs la matches record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; name of ground ; chester road north ground } ; worcs la matches } ; hop { filter_eq { all_rows ; name of ground ; racecourse ground } ; worcs la matches } }, interpretation: select the rows whose name of ground record fuzzily matches to chester road north ground . take the worcs la matches record of this row . select the rows whose name of ground record fuzzily matches to racecourse ground . take the worcs la matches record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; name of ground ; chester road north ground } ; worcs la matches } ; hop { filter_eq { all_rows ; name of ground ; racecourse ground } ; worcs la matches } }, interpretation: for the power rpm records of all rows , most of them are greater than or equal to 6000 .
Output: no
Command: most_greater_eq { all_rows ; power rpm ; 6000 }, interpretation: for the power rpm records of all rows , most of them are greater than or equal to 6000 .
Output: yes
Command: most_greater_eq { all_rows ; power rpm ; 6000 }, interpretation: select the row whose international tourism expenditure ( 2012 ) record of all rows is 1st maximum . the country record of this row is china .
Output: no
Command: eq { hop { nth_argmax { all_rows ; international tourism expenditure ( 2012 ) ; 1 } ; country } ; china }, interpretation: select the row whose international tourism expenditure ( 2012 ) record of all rows is 1st maximum . the country record of this row is china .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; international tourism expenditure ( 2012 ) ; 1 } ; country } ; china }, interpretation: select the rows whose playoffs record fuzzily matches to league finals . there is only one such row in the table . the year record of this unqiue row is 1978 .
Output: no
Command: and { only { filter_eq { all_rows ; playoffs ; league finals } } ; eq { hop { filter_eq { all_rows ; playoffs ; league finals } ; year } ; 1978 } }, interpretation: select the rows whose playoffs record fuzzily matches to league finals . there is only one such row in the table . the year record of this unqiue row is 1978 .
Output: yes
Command: and { only { filter_eq { all_rows ; playoffs ; league finals } } ; eq { hop { filter_eq { all_rows ; playoffs ; league finals } ; year } ; 1978 } }, interpretation: for the winning team records of all rows , most of them fuzzily match to mobilecast impul .
Output: no
Command: most_eq { all_rows ; winning team ; mobilecast impul }, interpretation: for the winning team records of all rows , most of them fuzzily match to mobilecast impul .
Output: yes
Command: most_eq { all_rows ; winning team ; mobilecast impul }, interpretation: select the rows whose game record is arbitrary . the number of such rows is 15 .
Output: no
Command: eq { count { filter_all { all_rows ; game } } ; 15 }, interpretation: select the rows whose game record is arbitrary . the number of such rows is 15 .
Output: yes
Command: eq { count { filter_all { all_rows ; game } } ; 15 }, interpretation: select the rows whose directedby record fuzzily matches to guy ferland . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; directedby ; guy ferland } } ; 2 }, interpretation: select the rows whose directedby record fuzzily matches to guy ferland . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; directedby ; guy ferland } } ; 2 }, interpretation: select the rows whose constructor record fuzzily matches to honda . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; constructor ; honda } } ; 2 }, interpretation: select the rows whose constructor record fuzzily matches to honda . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; constructor ; honda } } ; 2 }, interpretation: select the row whose place record of all rows is minimum . the artist record of this row is liam reilly . the song record of this row is somewhere in europe .
Output: no
Command: and { eq { hop { argmin { all_rows ; place } ; artist } ; liam reilly } ; eq { hop { argmin { all_rows ; place } ; song } ; somewhere in europe } }, interpretation: select the row whose place record of all rows is minimum . the artist record of this row is liam reilly . the song record of this row is somewhere in europe .
Output: yes