niryuu/sni-each-converted huggingface dataset:
Command: eq { count { filter_eq { all_rows ; date ; 1997 } } ; 2 }, interpretation: select the rows whose crowd record is greater than 20000 . among these rows , select the rows whose venue record fuzzily matches to victoria park . there is only one such row in the table . the home team record of this unqiue row is collingwood .
Output: no
Command: and { only { filter_eq { filter_greater { all_rows ; crowd ; 20000 } ; venue ; victoria park } } ; eq { hop { filter_eq { filter_greater { all_rows ; crowd ; 20000 } ; venue ; victoria park } ; home team } ; collingwood } }, interpretation: select the rows whose crowd record is greater than 20000 . among these rows , select the rows whose venue record fuzzily matches to victoria park . there is only one such row in the table . the home team record of this unqiue row is collingwood .
Output: yes
Command: and { only { filter_eq { filter_greater { all_rows ; crowd ; 20000 } ; venue ; victoria park } } ; eq { hop { filter_eq { filter_greater { all_rows ; crowd ; 20000 } ; venue ; victoria park } ; home team } ; collingwood } }, interpretation: select the rows whose city record fuzzily matches to santa clara , california . there is only one such row in the table . the athlete record of this unqiue row is mike ryan .
Output: no
Command: and { only { filter_eq { all_rows ; city ; santa clara , california } } ; eq { hop { filter_eq { all_rows ; city ; santa clara , california } ; athlete } ; mike ryan } }, interpretation: select the rows whose city record fuzzily matches to santa clara , california . there is only one such row in the table . the athlete record of this unqiue row is mike ryan .
Output: yes
Command: and { only { filter_eq { all_rows ; city ; santa clara , california } } ; eq { hop { filter_eq { all_rows ; city ; santa clara , california } ; athlete } ; mike ryan } }, interpretation: select the rows whose team ( league ) record fuzzily matches to lake superior state university ( ncaa ) . there is only one such row in the table . the player record of this unqiue row is paul constantin .
Output: no
Command: and { only { filter_eq { all_rows ; team ( league ) ; lake superior state university ( ncaa ) } } ; eq { hop { filter_eq { all_rows ; team ( league ) ; lake superior state university ( ncaa ) } ; player } ; paul constantin } }, interpretation: select the rows whose team ( league ) record fuzzily matches to lake superior state university ( ncaa ) . there is only one such row in the table . the player record of this unqiue row is paul constantin .
Output: yes
Command: and { only { filter_eq { all_rows ; team ( league ) ; lake superior state university ( ncaa ) } } ; eq { hop { filter_eq { all_rows ; team ( league ) ; lake superior state university ( ncaa ) } ; player } ; paul constantin } }, interpretation: the 12th minimum pick record of all rows is 12 . the player record of the row with 12th minimum pick record is john curtis .
Output: no
Command: and { eq { nth_min { all_rows ; pick ; 12 } ; 12 } ; eq { hop { nth_argmin { all_rows ; pick ; 12 } ; player } ; john curtis } }, interpretation: the 12th minimum pick record of all rows is 12 . the player record of the row with 12th minimum pick record is john curtis .
Output: yes
Command: and { eq { nth_min { all_rows ; pick ; 12 } ; 12 } ; eq { hop { nth_argmin { all_rows ; pick ; 12 } ; player } ; john curtis } }, interpretation: select the rows whose tournament record fuzzily matches to the open championship . there is only one such row in the table . the wins record of this unqiue row is 1 .
Output: no
Command: and { only { filter_eq { all_rows ; tournament ; the open championship } } ; eq { hop { filter_eq { all_rows ; tournament ; the open championship } ; wins } ; 1 } }, interpretation: the 1st minimum date record of all rows is 03 oct . the time record of the row with 1st minimum date record is 12:00 .
Output: no
Command: and { eq { nth_min { all_rows ; date ; 1 } ; 03 oct } ; eq { hop { nth_argmin { all_rows ; date ; 1 } ; time } ; 12:00 } }, interpretation: the 1st minimum date record of all rows is 03 oct . the time record of the row with 1st minimum date record is 12:00 .
Output: yes
Command: and { eq { nth_min { all_rows ; date ; 1 } ; 03 oct } ; eq { hop { nth_argmin { all_rows ; date ; 1 } ; time } ; 12:00 } }, interpretation: select the rows whose lms class record fuzzily matches to 3p . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; lms class ; 3p } } ; 2 }, interpretation: select the rows whose lms class record fuzzily matches to 3p . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; lms class ; 3p } } ; 2 }, interpretation: the average of the score record of all rows is 2.2 .
Output: no
Command: round_eq { avg { all_rows ; score } ; 2.2 }, interpretation: the average of the score record of all rows is 2.2 .
Output: yes
Command: round_eq { avg { all_rows ; score } ; 2.2 }, interpretation: select the rows whose opponent record fuzzily matches to brooklyn dodgers . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; opponent ; brooklyn dodgers } } ; 2 }, interpretation: select the rows whose opponent record fuzzily matches to brooklyn dodgers . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; opponent ; brooklyn dodgers } } ; 2 }, interpretation: for the engine records of all rows , most of them fuzzily match to ferrari v12 .
Output: no
Command: most_eq { all_rows ; engine ; ferrari v12 }, interpretation: for the engine records of all rows , most of them fuzzily match to ferrari v12 .
Output: yes
Command: most_eq { all_rows ; engine ; ferrari v12 }, interpretation: select the rows whose region record fuzzily matches to middle east . the maximum joined opec record of these rows is 1967 . the country record of the row with superlative joined opec record is united arab emirates .
Output: no
Command: and { eq { max { filter_eq { all_rows ; region ; middle east } ; joined opec } ; 1967 } ; eq { hop { argmax { filter_eq { all_rows ; region ; middle east } ; joined opec } ; country } ; united arab emirates } }, interpretation: select the rows whose region record fuzzily matches to middle east . the maximum joined opec record of these rows is 1967 . the country record of the row with superlative joined opec record is united arab emirates .
Output: yes
Command: and { eq { max { filter_eq { all_rows ; region ; middle east } ; joined opec } ; 1967 } ; eq { hop { argmax { filter_eq { all_rows ; region ; middle east } ; joined opec } ; country } ; united arab emirates } }, interpretation: select the row whose founded record of all rows is minimum . the team record of this row is northern demons .
Output: no
Command: eq { hop { argmin { all_rows ; founded } ; team } ; northern demons }, interpretation: select the row whose founded record of all rows is minimum . the team record of this row is northern demons .
Output: yes
Command: eq { hop { argmin { all_rows ; founded } ; team } ; northern demons }, interpretation: select the rows whose artist record fuzzily matches to kerri burnett . there is only one such row in the table . the theme record of this unqiue row is trumpeter swan .
Output: no
Command: and { only { filter_eq { all_rows ; artist ; kerri burnett } } ; eq { hop { filter_eq { all_rows ; artist ; kerri burnett } ; theme } ; trumpeter swan } }, interpretation: select the rows whose artist record fuzzily matches to kerri burnett . there is only one such row in the table . the theme record of this unqiue row is trumpeter swan .
Output: yes
Command: and { only { filter_eq { all_rows ; artist ; kerri burnett } } ; eq { hop { filter_eq { all_rows ; artist ; kerri burnett } ; theme } ; trumpeter swan } }, interpretation: select the rows whose player record fuzzily matches to bob tway . take the year ( s ) won record of this row . select the rows whose player record fuzzily matches to phil mickelson . take the year ( s ) won record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; player ; bob tway } ; year ( s ) won } ; hop { filter_eq { all_rows ; player ; phil mickelson } ; year ( s ) won } }, interpretation: select the rows whose player record fuzzily matches to bob tway . take the year ( s ) won record of this row . select the rows whose player record fuzzily matches to phil mickelson . take the year ( s ) won record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; player ; bob tway } ; year ( s ) won } ; hop { filter_eq { all_rows ; player ; phil mickelson } ; year ( s ) won } }, interpretation: select the rows whose nationality record fuzzily matches to milanese . there is only one such row in the table . the elector record of this unqiue row is simone borsano .
Output: no
Command: and { only { filter_eq { all_rows ; nationality ; milanese } } ; eq { hop { filter_eq { all_rows ; nationality ; milanese } ; elector } ; simone borsano } }, interpretation: select the rows whose nationality record fuzzily matches to milanese . there is only one such row in the table . the elector record of this unqiue row is simone borsano .
Output: yes
Command: and { only { filter_eq { all_rows ; nationality ; milanese } } ; eq { hop { filter_eq { all_rows ; nationality ; milanese } ; elector } ; simone borsano } }, interpretation: select the rows whose player record fuzzily matches to lionel cann . take the runs record of this row . select the rows whose player record fuzzily matches to dean minors . take the runs record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; player ; lionel cann } ; runs } ; hop { filter_eq { all_rows ; player ; dean minors } ; runs } }, interpretation: select the rows whose player record fuzzily matches to lionel cann . take the runs record of this row . select the rows whose player record fuzzily matches to dean minors . take the runs record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; player ; lionel cann } ; runs } ; hop { filter_eq { all_rows ; player ; dean minors } ; runs } }, interpretation: for the platform ( s ) records of all rows , most of them fuzzily match to playstation 3 .
Output: no
Command: most_eq { all_rows ; platform ( s ) ; playstation 3 }, interpretation: for the platform ( s ) records of all rows , most of them fuzzily match to playstation 3 .
Output: yes
Command: most_eq { all_rows ; platform ( s ) ; playstation 3 }, interpretation: for the video records of all rows , most of them fuzzily match to 480i .
Output: no
Command: most_eq { all_rows ; video ; 480i }, interpretation: for the video records of all rows , most of them fuzzily match to 480i .
Output: yes
Command: most_eq { all_rows ; video ; 480i }, interpretation: select the rows whose name record fuzzily matches to quinn pitcock . take the round record of this row . select the rows whose name record fuzzily matches to roy hall . take the round record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; name ; quinn pitcock } ; round } ; hop { filter_eq { all_rows ; name ; roy hall } ; round } }, interpretation: select the rows whose name record fuzzily matches to quinn pitcock . take the round record of this row . select the rows whose name record fuzzily matches to roy hall . take the round record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; name ; quinn pitcock } ; round } ; hop { filter_eq { all_rows ; name ; roy hall } ; round } }, interpretation: select the row whose us viewers ( million ) record of all rows is minimum . the title record of this row is the lorelais ' first day at yale .
Output: no
Command: eq { hop { argmin { all_rows ; us viewers ( million ) } ; title } ; the lorelais ' first day at yale }, interpretation: select the row whose us viewers ( million ) record of all rows is minimum . the title record of this row is the lorelais ' first day at yale .
Output: yes
Command: eq { hop { argmin { all_rows ; us viewers ( million ) } ; title } ; the lorelais ' first day at yale }, interpretation: select the rows whose field goals record is not equal to 0 . there is only one such row in the table . the player record of this unqiue row is octy graham .
Output: no
Command: and { only { filter_not_eq { all_rows ; field goals ; 0 } } ; eq { hop { filter_not_eq { all_rows ; field goals ; 0 } ; player } ; octy graham } }, interpretation: select the rows whose field goals record is not equal to 0 . there is only one such row in the table . the player record of this unqiue row is octy graham .
Output: yes
Command: and { only { filter_not_eq { all_rows ; field goals ; 0 } } ; eq { hop { filter_not_eq { all_rows ; field goals ; 0 } ; player } ; octy graham } }, interpretation: for the intra - molecular structure records of all rows , most of them fuzzily match to no .
Output: no
Command: most_eq { all_rows ; intra - molecular structure ; no }, interpretation: for the intra - molecular structure records of all rows , most of them fuzzily match to no .
Output: yes
Command: most_eq { all_rows ; intra - molecular structure ; no }, interpretation: select the row whose pts record of all rows is maximum . the year record of this row is 1995 .
Output: no
Command: eq { hop { argmax { all_rows ; pts } ; year } ; 1995 }, interpretation: select the row whose pts record of all rows is maximum . the year record of this row is 1995 .
Output: yes
Command: eq { hop { argmax { all_rows ; pts } ; year } ; 1995 }, interpretation: select the rows whose no in season record fuzzily matches to 1 . take the viewers ( millions ) record of this row . select the rows whose no in season record fuzzily matches to 12 . take the viewers ( millions ) record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; no in season ; 1 } ; viewers ( millions ) } ; hop { filter_eq { all_rows ; no in season ; 12 } ; viewers ( millions ) } }, interpretation: select the rows whose no in season record fuzzily matches to 1 . take the viewers ( millions ) record of this row . select the rows whose no in season record fuzzily matches to 12 . take the viewers ( millions ) record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; no in season ; 1 } ; viewers ( millions ) } ; hop { filter_eq { all_rows ; no in season ; 12 } ; viewers ( millions ) } }, interpretation: the average of the score record of all rows is 139 .
Output: no
Command: round_eq { avg { all_rows ; score } ; 139 }, interpretation: the average of the score record of all rows is 139 .
Output: yes
Command: round_eq { avg { all_rows ; score } ; 139 }, interpretation: select the row whose swing to gain record of all rows is 3rd maximum . the constituency record of this row is ayr .
Output: no
Command: eq { hop { nth_argmax { all_rows ; swing to gain ; 3 } ; constituency } ; ayr }, interpretation: select the row whose swing to gain record of all rows is 3rd maximum . the constituency record of this row is ayr .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; swing to gain ; 3 } ; constituency } ; ayr }, interpretation: select the rows whose year record is equal to 2013 . there is only one such row in the table . the nominated work record of this unqiue row is commitment .
Output: no
Command: and { only { filter_eq { all_rows ; year ; 2013 } } ; eq { hop { filter_eq { all_rows ; year ; 2013 } ; nominated work } ; commitment } }, interpretation: select the rows whose year record is equal to 2013 . there is only one such row in the table . the nominated work record of this unqiue row is commitment .
Output: yes
Command: and { only { filter_eq { all_rows ; year ; 2013 } } ; eq { hop { filter_eq { all_rows ; year ; 2013 } ; nominated work } ; commitment } }, interpretation: select the rows whose decade record fuzzily matches to 2000s . there is only one such row in the table . the song title record of this unqiue row is dirty little secret .
Output: no
Command: and { only { filter_eq { all_rows ; decade ; 2000s } } ; eq { hop { filter_eq { all_rows ; decade ; 2000s } ; song title } ; dirty little secret } }, interpretation: select the rows whose decade record fuzzily matches to 2000s . there is only one such row in the table . the song title record of this unqiue row is dirty little secret .
Output: yes
Command: and { only { filter_eq { all_rows ; decade ; 2000s } } ; eq { hop { filter_eq { all_rows ; decade ; 2000s } ; song title } ; dirty little secret } }, interpretation: select the rows whose date record fuzzily matches to october . select the row whose week record of these rows is 3rd minimum . the opponent record of this row is san francisco 49ers . the result record of this row is w 31 - 17 .
Output: no
Command: and { eq { hop { nth_argmin { filter_eq { all_rows ; date ; october } ; week ; 3 } ; opponent } ; san francisco 49ers } ; eq { hop { nth_argmin { filter_eq { all_rows ; date ; october } ; week ; 3 } ; result } ; w 31 - 17 } }, interpretation: select the rows whose date record fuzzily matches to october . select the row whose week record of these rows is 3rd minimum . the opponent record of this row is san francisco 49ers . the result record of this row is w 31 - 17 .
Output: yes
Command: and { eq { hop { nth_argmin { filter_eq { all_rows ; date ; october } ; week ; 3 } ; opponent } ; san francisco 49ers } ; eq { hop { nth_argmin { filter_eq { all_rows ; date ; october } ; week ; 3 } ; result } ; w 31 - 17 } }, interpretation: select the rows whose candidates record fuzzily matches to david bard . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; candidates ; david bard } } ; 2 }, interpretation: select the rows whose candidates record fuzzily matches to david bard . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; candidates ; david bard } } ; 2 }, interpretation: select the rows whose outcome record fuzzily matches to winner . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; outcome ; winner } } ; 4 }, interpretation: select the rows whose outcome record fuzzily matches to winner . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; outcome ; winner } } ; 4 }, interpretation: for the points records of all rows , most of them are less than 10 .
Output: no
Command: most_less { all_rows ; points ; 10 }, interpretation: select the row whose gold record of all rows is maximum . the nation record of this row is united states .
Output: no
Command: eq { hop { argmax { all_rows ; gold } ; nation } ; united states }, interpretation: select the row whose gold record of all rows is maximum . the nation record of this row is united states .
Output: yes
Command: eq { hop { argmax { all_rows ; gold } ; nation } ; united states }, interpretation: select the row whose year established record of all rows is minimum . the members record of this row is university of tübingen .
Output: no
Command: eq { hop { argmin { all_rows ; year established } ; members } ; university of tübingen }, interpretation: select the row whose year established record of all rows is minimum . the members record of this row is university of tübingen .
Output: yes
Command: eq { hop { argmin { all_rows ; year established } ; members } ; university of tübingen }, interpretation: select the rows whose opponent record fuzzily matches to new york jets . select the row whose date record of these rows is 2nd minimum . the bills points record of this row is 34 .
Output: no
Command: eq { hop { nth_argmin { filter_eq { all_rows ; opponent ; new york jets } ; date ; 2 } ; bills points } ; 34 }, interpretation: select the rows whose opponent record fuzzily matches to new york jets . select the row whose date record of these rows is 2nd minimum . the bills points record of this row is 34 .
Output: yes
Command: eq { hop { nth_argmin { filter_eq { all_rows ; opponent ; new york jets } ; date ; 2 } ; bills points } ; 34 }, interpretation: the average of the crowd record of all rows is 16000 .
Output: no
Command: round_eq { avg { all_rows ; crowd } ; 16000 }, interpretation: select the row whose crowd record of all rows is maximum . the venue record of this row is vfl park .
Output: no
Command: eq { hop { argmax { all_rows ; crowd } ; venue } ; vfl park }, interpretation: select the row whose crowd record of all rows is maximum . the venue record of this row is vfl park .
Output: yes
Command: eq { hop { argmax { all_rows ; crowd } ; venue } ; vfl park }, interpretation: the average of the attendance record of all rows is 50540 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 50540 }, interpretation: the average of the attendance record of all rows is 50540 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 50540 }, interpretation: select the rows whose player record is arbitrary . the number of such rows is 17 .
Output: no
Command: eq { count { filter_all { all_rows ; player } } ; 17 }, interpretation: select the rows whose player record is arbitrary . the number of such rows is 17 .
Output: yes
Command: eq { count { filter_all { all_rows ; player } } ; 17 }, interpretation: select the rows whose airline record fuzzily matches to tam . take the rank record of this row . select the rows whose airline record fuzzily matches to gol . take the rank record of this row . the second record is 1 better larger than the first record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; airline ; tam } ; rank } ; hop { filter_eq { all_rows ; airline ; gol } ; rank } } ; -1 better }, interpretation: select the rows whose airline record fuzzily matches to tam . take the rank record of this row . select the rows whose airline record fuzzily matches to gol . take the rank record of this row . the second record is 1 better larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; airline ; tam } ; rank } ; hop { filter_eq { all_rows ; airline ; gol } ; rank } } ; -1 better }, interpretation: select the rows whose away team record fuzzily matches to richmond . take the away team score record of this row . select the rows whose away team record fuzzily matches to hawthorn . take the away team score record of this row . the first record is greater than the second record . the away team score record of the first row is 7.9 ( 51 ) . the away team score record of the second row is 7.7 ( 49 ) .
Output: no
Command: and { greater { hop { filter_eq { all_rows ; away team ; richmond } ; away team score } ; hop { filter_eq { all_rows ; away team ; hawthorn } ; away team score } } ; and { eq { hop { filter_eq { all_rows ; away team ; richmond } ; away team score } ; 7.9 ( 51 ) } ; eq { hop { filter_eq { all_rows ; away team ; hawthorn } ; away team score } ; 7.7 ( 49 ) } } }, interpretation: select the rows whose away team record fuzzily matches to richmond . take the away team score record of this row . select the rows whose away team record fuzzily matches to hawthorn . take the away team score record of this row . the first record is greater than the second record . the away team score record of the first row is 7.9 ( 51 ) . the away team score record of the second row is 7.7 ( 49 ) .
Output: yes
Command: and { greater { hop { filter_eq { all_rows ; away team ; richmond } ; away team score } ; hop { filter_eq { all_rows ; away team ; hawthorn } ; away team score } } ; and { eq { hop { filter_eq { all_rows ; away team ; richmond } ; away team score } ; 7.9 ( 51 ) } ; eq { hop { filter_eq { all_rows ; away team ; hawthorn } ; away team score } ; 7.7 ( 49 ) } } }, interpretation: select the row whose first elected record of all rows is 2nd minimum . the incumbent record of this row is s otis bland .
Output: no
Command: eq { hop { nth_argmin { all_rows ; first elected ; 2 } ; incumbent } ; s otis bland }, interpretation: select the row whose first elected record of all rows is 2nd minimum . the incumbent record of this row is s otis bland .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; first elected ; 2 } ; incumbent } ; s otis bland }, interpretation: select the rows whose engine type record fuzzily matches to diesel - electric . there is only one such row in the table . the make & model record of this unqiue row is orion vii 3rd generation .
Output: no
Command: and { only { filter_eq { all_rows ; engine type ; diesel - electric } } ; eq { hop { filter_eq { all_rows ; engine type ; diesel - electric } ; make & model } ; orion vii 3rd generation } }, interpretation: select the rows whose engine type record fuzzily matches to diesel - electric . there is only one such row in the table . the make & model record of this unqiue row is orion vii 3rd generation .
Output: yes
Command: and { only { filter_eq { all_rows ; engine type ; diesel - electric } } ; eq { hop { filter_eq { all_rows ; engine type ; diesel - electric } ; make & model } ; orion vii 3rd generation } }, interpretation: select the rows whose result record fuzzily matches to l . the 2nd minimum date record of these rows is october 11 , 1998 . the opponent record of the row with 2nd minimum date record is new england patriots .
Output: no
Command: and { eq { nth_min { filter_eq { all_rows ; result ; l } ; date ; 2 } ; october 11 , 1998 } ; eq { hop { nth_argmin { filter_eq { all_rows ; result ; l } ; date ; 2 } ; opponent } ; new england patriots } }, interpretation: select the rows whose result record fuzzily matches to l . the 2nd minimum date record of these rows is october 11 , 1998 . the opponent record of the row with 2nd minimum date record is new england patriots .
Output: yes
Command: and { eq { nth_min { filter_eq { all_rows ; result ; l } ; date ; 2 } ; october 11 , 1998 } ; eq { hop { nth_argmin { filter_eq { all_rows ; result ; l } ; date ; 2 } ; opponent } ; new england patriots } }, interpretation: select the row whose year record of all rows is 1st minimum . the name record of this row is franklin exchange building .
Output: no
Command: eq { hop { nth_argmin { all_rows ; year ; 1 } ; name } ; franklin exchange building }, interpretation: select the row whose year record of all rows is 1st minimum . the name record of this row is franklin exchange building .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; year ; 1 } ; name } ; franklin exchange building }, interpretation: the average of the margin of victory record of all rows is 2 .
Output: no
Command: round_eq { avg { all_rows ; margin of victory } ; 2 }, interpretation: the average of the margin of victory record of all rows is 2 .
Output: yes
Command: round_eq { avg { all_rows ; margin of victory } ; 2 }, interpretation: for the uccfs records of all rows , most of them are equal to 2006 .
Output: no
Command: most_eq { all_rows ; uccfs ; 2006 }, interpretation: for the uccfs records of all rows , most of them are equal to 2006 .
Output: yes
Command: most_eq { all_rows ; uccfs ; 2006 }, interpretation: select the rows whose native american record fuzzily matches to 0.0 % . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; native american ; 0.0 % } } ; 4 }, interpretation: select the rows whose native american record fuzzily matches to 0.0 % . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; native american ; 0.0 % } } ; 4 }, interpretation: for the points records of all rows , most of them are greater than or equal to 7 .
Output: no
Command: most_greater_eq { all_rows ; points ; 7 }, interpretation: for the points records of all rows , most of them are greater than or equal to 7 .
Output: yes
Command: most_greater_eq { all_rows ; points ; 7 }, interpretation: select the rows whose result record does not match to re-elected . there is only one such row in the table . the incumbent record of this unqiue row is iris faircloth blitch .
Output: no
Command: and { only { filter_not_eq { all_rows ; result ; re-elected } } ; eq { hop { filter_not_eq { all_rows ; result ; re-elected } ; incumbent } ; iris faircloth blitch } }, interpretation: select the rows whose result record does not match to re-elected . there is only one such row in the table . the incumbent record of this unqiue row is iris faircloth blitch .
Output: yes
Command: and { only { filter_not_eq { all_rows ; result ; re-elected } } ; eq { hop { filter_not_eq { all_rows ; result ; re-elected } ; incumbent } ; iris faircloth blitch } }, interpretation: select the row whose distance ( km ) record of all rows is minimum . the station record of this row is kawarada .
Output: no
Command: eq { hop { argmin { all_rows ; distance ( km ) } ; station } ; kawarada }, interpretation: select the row whose distance ( km ) record of all rows is minimum . the station record of this row is kawarada .
Output: yes
Command: eq { hop { argmin { all_rows ; distance ( km ) } ; station } ; kawarada }, interpretation: select the rows whose venue record is arbitrary . the number of such rows is 6 .
Output: no
Command: eq { count { filter_all { all_rows ; venue } } ; 6 }, interpretation: select the rows whose county record fuzzily matches to new haven . for the proposed records of these rows , most of them are less than 2000 .
Output: no
Command: most_less { filter_eq { all_rows ; county ; new haven } ; proposed ; 2000 }, interpretation: select the rows whose county record fuzzily matches to new haven . for the proposed records of these rows , most of them are less than 2000 .
Output: yes
Command: most_less { filter_eq { all_rows ; county ; new haven } ; proposed ; 2000 }, interpretation: select the rows whose altitude ( mslm ) record is greater than 100 . among these rows , select the rows whose population record is less than 10000 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_less { filter_greater { all_rows ; altitude ( mslm ) ; 100 } ; population ; 10000 } } ; 2 }, interpretation: select the rows whose altitude ( mslm ) record is greater than 100 . among these rows , select the rows whose population record is less than 10000 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_less { filter_greater { all_rows ; altitude ( mslm ) ; 100 } ; population ; 10000 } } ; 2 }, interpretation: select the row whose attendance record of all rows is 1st maximum . the opponent record of this row is new york giants .
Output: no
Command: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; opponent } ; new york giants }, interpretation: select the row whose attendance record of all rows is 1st maximum . the opponent record of this row is new york giants .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; opponent } ; new york giants }, interpretation: select the row whose upper index kcal / nm 3 record of all rows is 2nd maximum . the fuel gas record of this row is iso - butane .
Output: no
Command: eq { hop { nth_argmax { all_rows ; upper index kcal / nm 3 ; 2 } ; fuel gas } ; iso - butane }, interpretation: select the row whose upper index kcal / nm 3 record of all rows is 2nd maximum . the fuel gas record of this row is iso - butane .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; upper index kcal / nm 3 ; 2 } ; fuel gas } ; iso - butane }, interpretation: select the rows whose manufacturer record fuzzily matches to kawasaki . there is only one such row in the table .
Output: no
Command: only { filter_eq { all_rows ; manufacturer ; kawasaki } }, interpretation: select the rows whose manufacturer record fuzzily matches to kawasaki . there is only one such row in the table .
Output: yes
Command: only { filter_eq { all_rows ; manufacturer ; kawasaki } }, interpretation: for the publisher records of all rows , most of them fuzzily match to nintendo .
Output: no
Command: most_eq { all_rows ; publisher ; nintendo }, interpretation: for the publisher records of all rows , most of them fuzzily match to nintendo .
Output: yes
Command: most_eq { all_rows ; publisher ; nintendo }, interpretation: select the rows whose high points record fuzzily matches to vince carter . the number of such rows is 12 .
Output: no
Command: eq { count { filter_eq { all_rows ; high points ; vince carter } } ; 12 }, interpretation: select the rows whose high points record fuzzily matches to vince carter . the number of such rows is 12 .
Output: yes
Command: eq { count { filter_eq { all_rows ; high points ; vince carter } } ; 12 }, interpretation: the average of the first downs record of all rows is 19.42 .
Output: no
Command: round_eq { avg { all_rows ; first downs } ; 19.42 }, interpretation: the average of the first downs record of all rows is 19.42 .
Output: yes
Command: round_eq { avg { all_rows ; first downs } ; 19.42 }, interpretation: for the network records of all rows , all of them fuzzily match to abc .
Output: no
Command: all_eq { all_rows ; network ; abc }, interpretation: for the network records of all rows , all of them fuzzily match to abc .
Output: yes
Command: all_eq { all_rows ; network ; abc }, interpretation: select the rows whose nation record fuzzily matches to puerto rico . take the bronze record of this row . select the rows whose nation record fuzzily matches to barbados . take the bronze record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; nation ; puerto rico } ; bronze } ; hop { filter_eq { all_rows ; nation ; barbados } ; bronze } }, interpretation: select the rows whose nation record fuzzily matches to puerto rico . take the bronze record of this row . select the rows whose nation record fuzzily matches to barbados . take the bronze record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; nation ; puerto rico } ; bronze } ; hop { filter_eq { all_rows ; nation ; barbados } ; bronze } }, interpretation: select the row whose total record of all rows is 2nd maximum . the name record of this row is michael doyle .
Output: no
Command: eq { hop { nth_argmax { all_rows ; total ; 2 } ; name } ; michael doyle }, interpretation: select the row whose total record of all rows is 2nd maximum . the name record of this row is michael doyle .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; total ; 2 } ; name } ; michael doyle }, interpretation: the average of the population record of all rows is 338662 .
Output: no
Command: round_eq { avg { all_rows ; population } ; 338662 }, interpretation: the average of the population record of all rows is 338662 .
Output: yes
Command: round_eq { avg { all_rows ; population } ; 338662 }, interpretation: the average of the drawn record of all rows is 2.55 .
Output: no
Command: round_eq { avg { all_rows ; drawn } ; 2.55 }, interpretation: the average of the drawn record of all rows is 2.55 .
Output: yes
Command: round_eq { avg { all_rows ; drawn } ; 2.55 }, interpretation: the average of the earnings record of all rows is $ 8,661,168.40 .
Output: no
Command: round_eq { avg { all_rows ; earnings } ; $ 8,661,168.40 }, interpretation: the average of the earnings record of all rows is $ 8,661,168.40 .
Output: yes
Command: round_eq { avg { all_rows ; earnings } ; $ 8,661,168.40 }, interpretation: select the rows whose format record fuzzily matches to black gospel . there is only one such row in the table . the call letters record of this unqiue row is kees .
Output: no
Command: and { only { filter_eq { all_rows ; format ; black gospel } } ; eq { hop { filter_eq { all_rows ; format ; black gospel } ; call letters } ; kees } }, interpretation: select the rows whose format record fuzzily matches to black gospel . there is only one such row in the table . the call letters record of this unqiue row is kees .
Output: yes
Command: and { only { filter_eq { all_rows ; format ; black gospel } } ; eq { hop { filter_eq { all_rows ; format ; black gospel } ; call letters } ; kees } }, interpretation: select the rows whose date record fuzzily matches to november . for the result records of these rows , most of them fuzzily match to l .
Output: no
Command: most_eq { filter_eq { all_rows ; date ; november } ; result ; l }, interpretation: select the rows whose date record fuzzily matches to november . for the result records of these rows , most of them fuzzily match to l .
Output: yes
Command: most_eq { filter_eq { all_rows ; date ; november } ; result ; l }, interpretation: select the rows whose tournament record fuzzily matches to mci heritage golf classic . there is only one such row in the table . the date record of this unqiue row is 17 apr 1988 .
Output: no
Command: and { only { filter_eq { all_rows ; tournament ; mci heritage golf classic } } ; eq { hop { filter_eq { all_rows ; tournament ; mci heritage golf classic } ; date } ; 17 apr 1988 } }, interpretation: select the rows whose tournament record fuzzily matches to mci heritage golf classic . there is only one such row in the table . the date record of this unqiue row is 17 apr 1988 .
Output: yes
Command: and { only { filter_eq { all_rows ; tournament ; mci heritage golf classic } } ; eq { hop { filter_eq { all_rows ; tournament ; mci heritage golf classic } ; date } ; 17 apr 1988 } }, interpretation: for the partner records of all rows , most of them fuzzily match to app .
Output: no
Command: most_eq { all_rows ; partner ; app }, interpretation: for the partner records of all rows , most of them fuzzily match to app .
Output: yes
Command: most_eq { all_rows ; partner ; app }, interpretation: select the row whose time record of all rows is minimum . the rider record of this row is chas mortimer .
Output: no
Command: eq { hop { argmin { all_rows ; time } ; rider } ; chas mortimer }, interpretation: select the row whose time record of all rows is minimum . the rider record of this row is chas mortimer .
Output: yes
Command: eq { hop { argmin { all_rows ; time } ; rider } ; chas mortimer }, interpretation: the average of the televote / sms record of all rows is 8.33 .
Output: no
Command: round_eq { avg { all_rows ; televote / sms } ; 8.33 }, interpretation: the average of the televote / sms record of all rows is 8.33 .
Output: yes
Command: round_eq { avg { all_rows ; televote / sms } ; 8.33 }, interpretation: the average of the prize record of all rows is 3137419 .
Output: no
Command: round_eq { avg { all_rows ; prize } ; 3137419 }, interpretation: the average of the prize record of all rows is 3137419 .
Output: yes
Command: round_eq { avg { all_rows ; prize } ; 3137419 }, interpretation: select the rows whose arena record fuzzily matches to honda center . among these rows , select the rows whose attendance record is equal to 17174 . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; arena ; honda center } ; attendance ; 17174 } } ; 5 }, interpretation: select the rows whose arena record fuzzily matches to honda center . among these rows , select the rows whose attendance record is equal to 17174 . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; arena ; honda center } ; attendance ; 17174 } } ; 5 }, interpretation: select the rows whose location record fuzzily matches to casablanca . for the category records of these rows , all of them fuzzily match to touring car .
Output: no
Command: all_eq { filter_eq { all_rows ; location ; casablanca } ; category ; touring car }, interpretation: select the rows whose location record fuzzily matches to casablanca . for the category records of these rows , all of them fuzzily match to touring car .
Output: yes
Command: all_eq { filter_eq { all_rows ; location ; casablanca } ; category ; touring car }, interpretation: for the winning amount records of all rows , most of them fuzzily match to rs 10 , 00000 .
Output: no
Command: most_eq { all_rows ; winning amount ; rs 10 , 00000 }, interpretation: for the winning amount records of all rows , most of them fuzzily match to rs 10 , 00000 .
Output: yes
Command: most_eq { all_rows ; winning amount ; rs 10 , 00000 }, interpretation: select the rows whose name record fuzzily matches to sue . take the discovered record of this row . select the rows whose name record fuzzily matches to bucky . take the discovered record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; name ; sue } ; discovered } ; hop { filter_eq { all_rows ; name ; bucky } ; discovered } }, interpretation: select the rows whose name record fuzzily matches to sue . take the discovered record of this row . select the rows whose name record fuzzily matches to bucky . take the discovered record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; name ; sue } ; discovered } ; hop { filter_eq { all_rows ; name ; bucky } ; discovered } }, interpretation: select the row whose total record of all rows is maximum . the gymnast record of this row is yang yilin ( chn ) .
Output: no
Command: eq { hop { argmax { all_rows ; total } ; gymnast } ; yang yilin ( chn ) }, interpretation: select the row whose total record of all rows is maximum . the gymnast record of this row is yang yilin ( chn ) .
Output: yes
Command: eq { hop { argmax { all_rows ; total } ; gymnast } ; yang yilin ( chn ) }, interpretation: the average of the earnings record of all rows is $ 426,356.60 .
Output: no
Command: round_eq { avg { all_rows ; earnings } ; $ 426,356.60 }, interpretation: the average of the earnings record of all rows is $ 426,356.60 .
Output: yes
Command: round_eq { avg { all_rows ; earnings } ; $ 426,356.60 }, interpretation: the average of the enrollment record of all rows is 738 .
Output: no
Command: round_eq { avg { all_rows ; enrollment } ; 738 }, interpretation: the average of the enrollment record of all rows is 738 .
Output: yes
Command: round_eq { avg { all_rows ; enrollment } ; 738 }, interpretation: select the rows whose city record fuzzily matches to atlanta , ga . take the passengers record of this row . select the rows whose city record fuzzily matches to orlando , fl . take the passengers record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; city ; atlanta , ga } ; passengers } ; hop { filter_eq { all_rows ; city ; orlando , fl } ; passengers } }, interpretation: select the rows whose city record fuzzily matches to atlanta , ga . take the passengers record of this row . select the rows whose city record fuzzily matches to orlando , fl . take the passengers record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; city ; atlanta , ga } ; passengers } ; hop { filter_eq { all_rows ; city ; orlando , fl } ; passengers } }, interpretation: select the row whose race record of all rows is minimum . the season record of this row is 2011 .
Output: no
Command: eq { hop { argmin { all_rows ; race } ; season } ; 2011 }, interpretation: select the row whose race record of all rows is minimum . the season record of this row is 2011 .
Output: yes
Command: eq { hop { argmin { all_rows ; race } ; season } ; 2011 }, interpretation: select the rows whose first class team record fuzzily matches to new south wales . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; first class team ; new south wales } } ; 5 }, interpretation: select the rows whose first class team record fuzzily matches to new south wales . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { all_rows ; first class team ; new south wales } } ; 5 }, interpretation: select the rows whose constructor record fuzzily matches to ferrari . select the row whose time / retired record of these rows is minimum . the driver record of this row is rubens barrichello .
Output: no
Command: eq { hop { argmin { filter_eq { all_rows ; constructor ; ferrari } ; time / retired } ; driver } ; rubens barrichello }, interpretation: select the rows whose constructor record fuzzily matches to ferrari . select the row whose time / retired record of these rows is minimum . the driver record of this row is rubens barrichello .
Output: yes
Command: eq { hop { argmin { filter_eq { all_rows ; constructor ; ferrari } ; time / retired } ; driver } ; rubens barrichello }, interpretation: for the original air date records of all rows , most of them are less than or equal to february 2005 .
Output: no
Command: most_less_eq { all_rows ; original air date ; february 2005 }, interpretation: for the original air date records of all rows , most of them are less than or equal to february 2005 .
Output: yes
Command: most_less_eq { all_rows ; original air date ; february 2005 }, interpretation: select the rows whose stadium record fuzzily matches to rovaniemen keskuskenttä . take the capacity record of this row . select the rows whose stadium record fuzzily matches to sonera stadium . take the capacity record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; stadium ; rovaniemen keskuskenttä } ; capacity } ; hop { filter_eq { all_rows ; stadium ; sonera stadium } ; capacity } }, interpretation: select the rows whose stadium record fuzzily matches to rovaniemen keskuskenttä . take the capacity record of this row . select the rows whose stadium record fuzzily matches to sonera stadium . take the capacity record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; stadium ; rovaniemen keskuskenttä } ; capacity } ; hop { filter_eq { all_rows ; stadium ; sonera stadium } ; capacity } }, interpretation: select the row whose time record of all rows is maximum . the opponent record of this row is bob schrijber .
Output: no
Command: eq { hop { argmax { all_rows ; time } ; opponent } ; bob schrijber }, interpretation: select the row whose time record of all rows is maximum . the opponent record of this row is bob schrijber .
Output: yes
Command: eq { hop { argmax { all_rows ; time } ; opponent } ; bob schrijber }, interpretation: for the manufacturer records of all rows , most of them fuzzily match to chevrolet .
Output: no
Command: most_eq { all_rows ; manufacturer ; chevrolet }, interpretation: for the manufacturer records of all rows , most of them fuzzily match to chevrolet .
Output: yes
Command: most_eq { all_rows ; manufacturer ; chevrolet }, interpretation: the average of the scored record of all rows is 1.17 .
Output: no
Command: round_eq { avg { all_rows ; scored } ; 1.17 }, interpretation: the average of the scored record of all rows is 1.17 .
Output: yes
Command: round_eq { avg { all_rows ; scored } ; 1.17 }, interpretation: for the nationality records of all rows , most of them fuzzily match to canada .
Output: no
Command: most_eq { all_rows ; nationality ; canada }, interpretation: for the nationality records of all rows , most of them fuzzily match to canada .
Output: yes
Command: most_eq { all_rows ; nationality ; canada }, interpretation: select the rows whose team record fuzzily matches to yamaha . the average of the points record of these rows is 4.5 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; team ; yamaha } ; points } ; 4.5 }, interpretation: select the rows whose team record fuzzily matches to yamaha . the average of the points record of these rows is 4.5 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; team ; yamaha } ; points } ; 4.5 }, interpretation: select the row whose points record of all rows is 2nd maximum . the name record of this row is tom hilde .
Output: no
Command: eq { hop { nth_argmax { all_rows ; points ; 2 } ; name } ; tom hilde }, interpretation: select the row whose points record of all rows is 2nd maximum . the name record of this row is tom hilde .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; points ; 2 } ; name } ; tom hilde }, interpretation: the average of the crowd record of all rows is 22250 .
Output: no
Command: round_eq { avg { all_rows ; crowd } ; 22250 }, interpretation: the average of the crowd record of all rows is 22250 .
Output: yes