niryuu/sni-each-converted huggingface dataset:
Command: most_eq { all_rows ; winning party 2003 ; labour }, interpretation: select the rows whose country record fuzzily matches to belgium . take the total record of this row . select the rows whose country record fuzzily matches to brazil . take the total record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; country ; belgium } ; total } ; hop { filter_eq { all_rows ; country ; brazil } ; total } }, interpretation: select the rows whose country record fuzzily matches to belgium . take the total record of this row . select the rows whose country record fuzzily matches to brazil . take the total record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; country ; belgium } ; total } ; hop { filter_eq { all_rows ; country ; brazil } ; total } }, interpretation: the average of the viewing figure record of all rows is 6.73 million .
Output: no
Command: round_eq { avg { all_rows ; viewing figure } ; 6.73 million }, interpretation: the average of the viewing figure record of all rows is 6.73 million .
Output: yes
Command: round_eq { avg { all_rows ; viewing figure } ; 6.73 million }, interpretation: select the rows whose year record is equal to 1987 . the sum of the notes record of these rows is 4:27:46 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; year ; 1987 } ; notes } ; 4:27:46 }, interpretation: select the rows whose year record is equal to 1987 . the sum of the notes record of these rows is 4:27:46 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; year ; 1987 } ; notes } ; 4:27:46 }, interpretation: select the row whose imports record of all rows is maximum . the country record of this row is china .
Output: no
Command: eq { hop { argmax { all_rows ; imports } ; country } ; china }, interpretation: select the row whose imports record of all rows is maximum . the country record of this row is china .
Output: yes
Command: eq { hop { argmax { all_rows ; imports } ; country } ; china }, interpretation: select the row whose finish record of all rows is 2nd minimum . the player record of this row is jeff sluman .
Output: no
Command: eq { hop { nth_argmin { all_rows ; finish ; 2 } ; player } ; jeff sluman }, interpretation: select the row whose finish record of all rows is 2nd minimum . the player record of this row is jeff sluman .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; finish ; 2 } ; player } ; jeff sluman }, interpretation: select the rows whose season record is arbitrary . the number of such rows is 5 .
Output: no
Command: eq { count { filter_all { all_rows ; season } } ; 5 }, interpretation: select the rows whose national titles record is equal to 0 . there is only one such row in the table . the school record of this unqiue row is college of saint mary .
Output: no
Command: and { only { filter_eq { all_rows ; national titles ; 0 } } ; eq { hop { filter_eq { all_rows ; national titles ; 0 } ; school } ; college of saint mary } }, interpretation: select the rows whose national titles record is equal to 0 . there is only one such row in the table . the school record of this unqiue row is college of saint mary .
Output: yes
Command: and { only { filter_eq { all_rows ; national titles ; 0 } } ; eq { hop { filter_eq { all_rows ; national titles ; 0 } ; school } ; college of saint mary } }, interpretation: select the row whose elevation ( m ) record of all rows is 2nd maximum . the peak record of this row is snøhetta .
Output: no
Command: eq { hop { nth_argmax { all_rows ; elevation ( m ) ; 2 } ; peak } ; snøhetta }, interpretation: select the row whose elevation ( m ) record of all rows is 2nd maximum . the peak record of this row is snøhetta .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; elevation ( m ) ; 2 } ; peak } ; snøhetta }, interpretation: select the rows whose region record fuzzily matches to united states . there is only one such row in the table . the date record of this unqiue row is 17 march 2009 .
Output: no
Command: and { only { filter_eq { all_rows ; region ; united states } } ; eq { hop { filter_eq { all_rows ; region ; united states } ; date } ; 17 march 2009 } }, interpretation: select the rows whose region record fuzzily matches to united states . there is only one such row in the table . the date record of this unqiue row is 17 march 2009 .
Output: yes
Command: and { only { filter_eq { all_rows ; region ; united states } } ; eq { hop { filter_eq { all_rows ; region ; united states } ; date } ; 17 march 2009 } }, interpretation: select the rows whose nickname record fuzzily matches to polar bears . there is only one such row in the table . the school record of this unqiue row is jackson .
Output: no
Command: and { only { filter_eq { all_rows ; nickname ; polar bears } } ; eq { hop { filter_eq { all_rows ; nickname ; polar bears } ; school } ; jackson } }, interpretation: select the rows whose nickname record fuzzily matches to polar bears . there is only one such row in the table . the school record of this unqiue row is jackson .
Output: yes
Command: and { only { filter_eq { all_rows ; nickname ; polar bears } } ; eq { hop { filter_eq { all_rows ; nickname ; polar bears } ; school } ; jackson } }, interpretation: select the rows whose aspect record fuzzily matches to 16:9 . there is only one such row in the table . the channel record of this unqiue row is 31.1 .
Output: no
Command: and { only { filter_eq { all_rows ; aspect ; 16:9 } } ; eq { hop { filter_eq { all_rows ; aspect ; 16:9 } ; channel } ; 31.1 } }, interpretation: select the rows whose aspect record fuzzily matches to 16:9 . there is only one such row in the table . the channel record of this unqiue row is 31.1 .
Output: yes
Command: and { only { filter_eq { all_rows ; aspect ; 16:9 } } ; eq { hop { filter_eq { all_rows ; aspect ; 16:9 } ; channel } ; 31.1 } }, interpretation: select the rows whose route of administration record fuzzily matches to iv . among these rows , select the rows whose investigation record fuzzily matches to infection / inflammation imaging . there is only one such row in the table . the name record of this unqiue row is in111 - s leukocyte .
Output: no
Command: and { only { filter_eq { filter_eq { all_rows ; route of administration ; iv } ; investigation ; infection / inflammation imaging } } ; eq { hop { filter_eq { filter_eq { all_rows ; route of administration ; iv } ; investigation ; infection / inflammation imaging } ; name } ; in111 - s leukocyte } }, interpretation: select the rows whose route of administration record fuzzily matches to iv . among these rows , select the rows whose investigation record fuzzily matches to infection / inflammation imaging . there is only one such row in the table . the name record of this unqiue row is in111 - s leukocyte .
Output: yes
Command: and { only { filter_eq { filter_eq { all_rows ; route of administration ; iv } ; investigation ; infection / inflammation imaging } } ; eq { hop { filter_eq { filter_eq { all_rows ; route of administration ; iv } ; investigation ; infection / inflammation imaging } ; name } ; in111 - s leukocyte } }, interpretation: the average of the population record of all rows is 60970 .
Output: no
Command: round_eq { avg { all_rows ; population } ; 60970 }, interpretation: the average of the population record of all rows is 60970 .
Output: yes
Command: round_eq { avg { all_rows ; population } ; 60970 }, interpretation: the average of the pop record of all rows is 38009 .
Output: no
Command: round_eq { avg { all_rows ; pop } ; 38009 }, interpretation: the average of the pop record of all rows is 38009 .
Output: yes
Command: round_eq { avg { all_rows ; pop } ; 38009 }, interpretation: select the row whose date record of all rows is 2nd maximum . the tournament record of this row is augusta , united states .
Output: no
Command: eq { hop { nth_argmax { all_rows ; date ; 2 } ; tournament } ; augusta , united states }, interpretation: select the row whose date record of all rows is 2nd maximum . the tournament record of this row is augusta , united states .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; date ; 2 } ; tournament } ; augusta , united states }, interpretation: select the rows whose date record fuzzily matches to sunday , november 16 . take the attendance record of this row . select the rows whose date record fuzzily matches to monday , december 22 . 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 ; date ; sunday , november 16 } ; attendance } ; hop { filter_eq { all_rows ; date ; monday , december 22 } ; attendance } }, interpretation: select the rows whose date record fuzzily matches to sunday , november 16 . take the attendance record of this row . select the rows whose date record fuzzily matches to monday , december 22 . 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 ; date ; sunday , november 16 } ; attendance } ; hop { filter_eq { all_rows ; date ; monday , december 22 } ; attendance } }, interpretation: select the rows whose date record fuzzily matches to october . select the row whose attendance record of these rows is 1st maximum . the opponent record of this row is dallas cowboys .
Output: no
Command: eq { hop { nth_argmax { filter_eq { all_rows ; date ; october } ; attendance ; 1 } ; opponent } ; dallas cowboys }, interpretation: select the rows whose date record fuzzily matches to october . select the row whose attendance record of these rows is 1st maximum . the opponent record of this row is dallas cowboys .
Output: yes
Command: eq { hop { nth_argmax { filter_eq { all_rows ; date ; october } ; attendance ; 1 } ; opponent } ; dallas cowboys }, interpretation: select the rows whose bleeding time record fuzzily matches to unaffected . the number of such rows is 6 .
Output: no
Command: eq { count { filter_eq { all_rows ; bleeding time ; unaffected } } ; 6 }, interpretation: select the rows whose bleeding time record fuzzily matches to unaffected . the number of such rows is 6 .
Output: yes
Command: eq { count { filter_eq { all_rows ; bleeding time ; unaffected } } ; 6 }, interpretation: select the rows whose name record fuzzily matches to historic federal reserve bank . take the floors record of this row . select the rows whose name record fuzzily matches to kansas city power and light building . take the floors record of this row . the first record is less than the second record . the floors record of the first row is 16 . the floors record of the second row is 34 .
Output: no
Command: and { less { hop { filter_eq { all_rows ; name ; historic federal reserve bank } ; floors } ; hop { filter_eq { all_rows ; name ; kansas city power and light building } ; floors } } ; and { eq { hop { filter_eq { all_rows ; name ; historic federal reserve bank } ; floors } ; 16 } ; eq { hop { filter_eq { all_rows ; name ; kansas city power and light building } ; floors } ; 34 } } }, interpretation: select the rows whose name record fuzzily matches to historic federal reserve bank . take the floors record of this row . select the rows whose name record fuzzily matches to kansas city power and light building . take the floors record of this row . the first record is less than the second record . the floors record of the first row is 16 . the floors record of the second row is 34 .
Output: yes
Command: and { less { hop { filter_eq { all_rows ; name ; historic federal reserve bank } ; floors } ; hop { filter_eq { all_rows ; name ; kansas city power and light building } ; floors } } ; and { eq { hop { filter_eq { all_rows ; name ; historic federal reserve bank } ; floors } ; 16 } ; eq { hop { filter_eq { all_rows ; name ; kansas city power and light building } ; floors } ; 34 } } }, interpretation: select the row whose date record of all rows is maximum . the label record of this row is fantasy records .
Output: no
Command: eq { hop { argmax { all_rows ; date } ; label } ; fantasy records }, interpretation: select the row whose date record of all rows is maximum . the label record of this row is fantasy records .
Output: yes
Command: eq { hop { argmax { all_rows ; date } ; label } ; fantasy records }, interpretation: select the rows whose broadcast record fuzzily matches to espn . for the attendance records of these rows , most of them are less than 100000 .
Output: no
Command: most_less { filter_eq { all_rows ; broadcast ; espn } ; attendance ; 100000 }, interpretation: select the rows whose broadcast record fuzzily matches to espn . for the attendance records of these rows , most of them are less than 100000 .
Output: yes
Command: most_less { filter_eq { all_rows ; broadcast ; espn } ; attendance ; 100000 }, interpretation: select the row whose date record of all rows is 2nd minimum . the opponents record of this row is 1 . fc heidenheim .
Output: no
Command: eq { hop { nth_argmin { all_rows ; date ; 2 } ; opponents } ; 1 . fc heidenheim }, interpretation: select the row whose date record of all rows is 2nd minimum . the opponents record of this row is 1 . fc heidenheim .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; date ; 2 } ; opponents } ; 1 . fc heidenheim }, interpretation: for the yards records of all rows , most of them are greater than 20 .
Output: no
Command: most_greater { all_rows ; yards ; 20 }, interpretation: for the yards records of all rows , most of them are greater than 20 .
Output: yes
Command: most_greater { all_rows ; yards ; 20 }, interpretation: the maximum final score record of all rows is w 41 - 31 . the opponent record of the row with superlative final score record is hamburg sea devils .
Output: no
Command: and { eq { max { all_rows ; final score } ; w 41 - 31 } ; eq { hop { argmax { all_rows ; final score } ; opponent } ; hamburg sea devils } }, interpretation: the maximum final score record of all rows is w 41 - 31 . the opponent record of the row with superlative final score record is hamburg sea devils .
Output: yes
Command: and { eq { max { all_rows ; final score } ; w 41 - 31 } ; eq { hop { argmax { all_rows ; final score } ; opponent } ; hamburg sea devils } }, interpretation: the sum of the championships record of all rows is 1 .
Output: no
Command: round_eq { sum { all_rows ; championships } ; 1 }, interpretation: the sum of the championships record of all rows is 1 .
Output: yes
Command: round_eq { sum { all_rows ; championships } ; 1 }, interpretation: select the rows whose points record is equal to 17 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; points ; 17 } } ; 3 }, interpretation: select the rows whose points record is equal to 17 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; points ; 17 } } ; 3 }, interpretation: select the rows whose episode title record fuzzily matches to remember . take the original air date record of this row . select the rows whose episode title record fuzzily matches to big brotherly love . take the original air date record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; episode title ; remember } ; original air date } ; hop { filter_eq { all_rows ; episode title ; big brotherly love } ; original air date } }, interpretation: select the rows whose episode title record fuzzily matches to remember . take the original air date record of this row . select the rows whose episode title record fuzzily matches to big brotherly love . take the original air date record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; episode title ; remember } ; original air date } ; hop { filter_eq { all_rows ; episode title ; big brotherly love } ; original air date } }, interpretation: select the row whose titles record of all rows is maximum . the city record of this row is budapest .
Output: no
Command: eq { hop { argmax { all_rows ; titles } ; city } ; budapest }, interpretation: select the row whose titles record of all rows is maximum . the city record of this row is budapest .
Output: yes
Command: eq { hop { argmax { all_rows ; titles } ; city } ; budapest }, interpretation: select the rows whose episode record fuzzily matches to 4 . take the ratings ( kanto ) record of this row . select the rows whose episode record fuzzily matches to 3 . take the ratings ( kanto ) record of this row . the first record is less than the second record . the ratings ( kanto ) record of the first row is 4.3 . the ratings ( kanto ) record of the second row is 4.6 .
Output: no
Command: and { less { hop { filter_eq { all_rows ; episode ; 4 } ; ratings ( kanto ) } ; hop { filter_eq { all_rows ; episode ; 3 } ; ratings ( kanto ) } } ; and { eq { hop { filter_eq { all_rows ; episode ; 4 } ; ratings ( kanto ) } ; 4.3 } ; eq { hop { filter_eq { all_rows ; episode ; 3 } ; ratings ( kanto ) } ; 4.6 } } }, interpretation: select the rows whose episode record fuzzily matches to 4 . take the ratings ( kanto ) record of this row . select the rows whose episode record fuzzily matches to 3 . take the ratings ( kanto ) record of this row . the first record is less than the second record . the ratings ( kanto ) record of the first row is 4.3 . the ratings ( kanto ) record of the second row is 4.6 .
Output: yes
Command: and { less { hop { filter_eq { all_rows ; episode ; 4 } ; ratings ( kanto ) } ; hop { filter_eq { all_rows ; episode ; 3 } ; ratings ( kanto ) } } ; and { eq { hop { filter_eq { all_rows ; episode ; 4 } ; ratings ( kanto ) } ; 4.3 } ; eq { hop { filter_eq { all_rows ; episode ; 3 } ; ratings ( kanto ) } ; 4.6 } } }, interpretation: select the rows whose format record fuzzily matches to vinyl record . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; format ; vinyl record } } ; 2 }, interpretation: select the rows whose format record fuzzily matches to vinyl record . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; format ; vinyl record } } ; 2 }, interpretation: the average of the score record of all rows is 97.3 .
Output: no
Command: round_eq { avg { all_rows ; score } ; 97.3 }, interpretation: the average of the score record of all rows is 97.3 .
Output: yes
Command: round_eq { avg { all_rows ; score } ; 97.3 }, interpretation: for the winning car records of all rows , most of them fuzzily match to lotus .
Output: no
Command: most_eq { all_rows ; winning car ; lotus }, interpretation: for the winning car records of all rows , most of them fuzzily match to lotus .
Output: yes
Command: most_eq { all_rows ; winning car ; lotus }, interpretation: select the row whose td 's record of all rows is 2nd maximum . the player record of this row is lenzie jackson .
Output: no
Command: eq { hop { nth_argmax { all_rows ; td 's ; 2 } ; player } ; lenzie jackson }, interpretation: select the row whose td 's record of all rows is 2nd maximum . the player record of this row is lenzie jackson .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; td 's ; 2 } ; player } ; lenzie jackson }, interpretation: for the political party records of all rows , most of them fuzzily match to labour party .
Output: no
Command: most_eq { all_rows ; political party ; labour party }, interpretation: for the political party records of all rows , most of them fuzzily match to labour party .
Output: yes
Command: most_eq { all_rows ; political party ; labour party }, interpretation: the sum of the population census 2009 record of all rows is 4383743 .
Output: no
Command: round_eq { sum { all_rows ; population census 2009 } ; 4383743 }, interpretation: the sum of the population census 2009 record of all rows is 4383743 .
Output: yes
Command: round_eq { sum { all_rows ; population census 2009 } ; 4383743 }, interpretation: select the row whose attendance record of all rows is 1st maximum . the date record of this row is november 17 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; date } ; november 17 }, interpretation: select the row whose attendance record of all rows is 1st maximum . the date record of this row is november 17 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; date } ; november 17 }, interpretation: select the rows whose sport record fuzzily matches to wrestling . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; sport ; wrestling } } ; 2 }, interpretation: select the rows whose sport record fuzzily matches to wrestling . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; sport ; wrestling } } ; 2 }, interpretation: select the rows whose date record fuzzily matches to sunday . select the row whose week record of these rows is 6th minimum . the opponent record of this row is new york giants .
Output: no
Command: eq { hop { nth_argmin { filter_eq { all_rows ; date ; sunday } ; week ; 6 } ; opponent } ; new york giants }, interpretation: select the rows whose date record fuzzily matches to sunday . select the row whose week record of these rows is 6th minimum . the opponent record of this row is new york giants .
Output: yes
Command: eq { hop { nth_argmin { filter_eq { all_rows ; date ; sunday } ; week ; 6 } ; opponent } ; new york giants }, interpretation: for the nominated work records of all rows , most of them fuzzily match to 71 : into the fire .
Output: no
Command: most_eq { all_rows ; nominated work ; 71 : into the fire }, interpretation: for the nominated work records of all rows , most of them fuzzily match to 71 : into the fire .
Output: yes
Command: most_eq { all_rows ; nominated work ; 71 : into the fire }, interpretation: select the rows whose venue record fuzzily matches to neman . take the capacity record of this row . select the rows whose venue record fuzzily matches to darida . take the capacity record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; venue ; neman } ; capacity } ; hop { filter_eq { all_rows ; venue ; darida } ; capacity } }, interpretation: select the rows whose venue record fuzzily matches to neman . take the capacity record of this row . select the rows whose venue record fuzzily matches to darida . take the capacity record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; venue ; neman } ; capacity } ; hop { filter_eq { all_rows ; venue ; darida } ; capacity } }, interpretation: for the average attendance home records of all rows , most of them are less than 3200 .
Output: no
Command: most_less { all_rows ; average attendance home ; 3200 }, interpretation: for the average attendance home records of all rows , most of them are less than 3200 .
Output: yes
Command: most_less { all_rows ; average attendance home ; 3200 }, interpretation: select the row whose crowd record of all rows is 1st maximum . the venue record of this row is victoria park .
Output: no
Command: eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; victoria park }, interpretation: select the rows whose points record is greater than or equal to 20 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_greater_eq { all_rows ; points ; 20 } } ; 3 }, interpretation: select the rows whose points record is greater than or equal to 20 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_greater_eq { all_rows ; points ; 20 } } ; 3 }, interpretation: select the rows whose competition record fuzzily matches to world championships . the 2nd minimum year record of these rows is 2001 .
Output: no
Command: eq { nth_min { filter_eq { all_rows ; competition ; world championships } ; year ; 2 } ; 2001 }, interpretation: select the rows whose competition record fuzzily matches to world championships . the 2nd minimum year record of these rows is 2001 .
Output: yes
Command: eq { nth_min { filter_eq { all_rows ; competition ; world championships } ; year ; 2 } ; 2001 }, interpretation: select the rows whose l3 cache record fuzzily matches to 8 mb . the maximum frequency record of these rows is 3 ghz . the model number record of the row with superlative frequency record is core i7 - 3940xm .
Output: no
Command: and { eq { max { filter_eq { all_rows ; l3 cache ; 8 mb } ; frequency } ; 3 ghz } ; eq { hop { argmax { filter_eq { all_rows ; l3 cache ; 8 mb } ; frequency } ; model number } ; core i7 - 3940xm } }, interpretation: select the rows whose l3 cache record fuzzily matches to 8 mb . the maximum frequency record of these rows is 3 ghz . the model number record of the row with superlative frequency record is core i7 - 3940xm .
Output: yes
Command: and { eq { max { filter_eq { all_rows ; l3 cache ; 8 mb } ; frequency } ; 3 ghz } ; eq { hop { argmax { filter_eq { all_rows ; l3 cache ; 8 mb } ; frequency } ; model number } ; core i7 - 3940xm } }, interpretation: select the rows whose date record is greater than 31 december 2010 . among these rows , select the rows whose competition record fuzzily matches to euro 2012 qualifying . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { filter_greater { all_rows ; date ; 31 december 2010 } ; competition ; euro 2012 qualifying } } ; 3 }, interpretation: select the rows whose date record is greater than 31 december 2010 . among these rows , select the rows whose competition record fuzzily matches to euro 2012 qualifying . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { filter_greater { all_rows ; date ; 31 december 2010 } ; competition ; euro 2012 qualifying } } ; 3 }, interpretation: select the rows whose % buddhist record is not equal to - . there is only one such row in the table . the area record of this unqiue row is ladakh .
Output: no
Command: and { only { filter_not_eq { all_rows ; % buddhist ; - } } ; eq { hop { filter_not_eq { all_rows ; % buddhist ; - } ; area } ; ladakh } }, interpretation: select the rows whose % buddhist record is not equal to - . there is only one such row in the table . the area record of this unqiue row is ladakh .
Output: yes
Command: and { only { filter_not_eq { all_rows ; % buddhist ; - } } ; eq { hop { filter_not_eq { all_rows ; % buddhist ; - } ; area } ; ladakh } }, interpretation: select the rows whose high rebounds record fuzzily matches to garnett . the sum of the high rebounds record of these rows is 65 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; high rebounds ; garnett } ; high rebounds } ; 65 }, interpretation: select the rows whose high rebounds record fuzzily matches to garnett . the sum of the high rebounds record of these rows is 65 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; high rebounds ; garnett } ; high rebounds } ; 65 }, interpretation: the average of the % of popular vote record of all rows is 2.21 .
Output: no
Command: round_eq { avg { all_rows ; % of popular vote } ; 2.21 }, interpretation: the average of the % of popular vote record of all rows is 2.21 .
Output: yes
Command: round_eq { avg { all_rows ; % of popular vote } ; 2.21 }, interpretation: select the row whose date record of all rows is 9th minimum . the competition record of this row is friendly .
Output: no
Command: eq { hop { nth_argmin { all_rows ; date ; 9 } ; competition } ; friendly }, interpretation: select the row whose date record of all rows is 9th minimum . the competition record of this row is friendly .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; date ; 9 } ; competition } ; friendly }, interpretation: select the rows whose chassis record fuzzily matches to hesketh 308e . take the year record of this row . select the rows whose chassis record fuzzily matches to surtees ts19 . take the year record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; chassis ; hesketh 308e } ; year } ; hop { filter_eq { all_rows ; chassis ; surtees ts19 } ; year } }, interpretation: select the rows whose chassis record fuzzily matches to hesketh 308e . take the year record of this row . select the rows whose chassis record fuzzily matches to surtees ts19 . take the year record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; chassis ; hesketh 308e } ; year } ; hop { filter_eq { all_rows ; chassis ; surtees ts19 } ; year } }, interpretation: for the points records of all rows , most of them are greater than 100 .
Output: no
Command: most_greater { all_rows ; points ; 100 }, interpretation: for the points records of all rows , most of them are greater than 100 .
Output: yes
Command: most_greater { all_rows ; points ; 100 }, interpretation: select the row whose time record of all rows is 4th minimum . the country record of this row is bulgaria .
Output: no
Command: eq { hop { nth_argmin { all_rows ; time ; 4 } ; country } ; bulgaria }, interpretation: select the row whose time record of all rows is 4th minimum . the country record of this row is bulgaria .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; time ; 4 } ; country } ; bulgaria }, interpretation: select the rows whose team record fuzzily matches to pescarolo sport . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; team ; pescarolo sport } } ; 2 }, interpretation: select the rows whose team record fuzzily matches to pescarolo sport . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; team ; pescarolo sport } } ; 2 }, 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: for the points records of all rows , most of them are less than 10 .
Output: yes
Command: most_less { all_rows ; points ; 10 }, interpretation: select the rows whose directed by record fuzzily matches to michael lange . the number of such rows is 9 .
Output: no
Command: eq { count { filter_eq { all_rows ; directed by ; michael lange } } ; 9 }, interpretation: select the rows whose directed by record fuzzily matches to michael lange . the number of such rows is 9 .
Output: yes
Command: eq { count { filter_eq { all_rows ; directed by ; michael lange } } ; 9 }, interpretation: select the row whose gore % record of all rows is maximum . the county record of this row is clark .
Output: no
Command: eq { hop { argmax { all_rows ; gore % } ; county } ; clark }, interpretation: select the row whose gore % record of all rows is maximum . the county record of this row is clark .
Output: yes
Command: eq { hop { argmax { all_rows ; gore % } ; county } ; clark }, interpretation: select the row whose number of electorates ( 2003 ) record of all rows is 2nd maximum . the constituency number record of this row is 49 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; number of electorates ( 2003 ) ; 2 } ; constituency number } ; 49 }, interpretation: select the row whose number of electorates ( 2003 ) record of all rows is 2nd maximum . the constituency number record of this row is 49 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; number of electorates ( 2003 ) ; 2 } ; constituency number } ; 49 }, interpretation: select the rows whose street address record fuzzily matches to gold avenue sw . for the height ft / m records of these rows , all of them are greater than or equal to 180 .
Output: no
Command: all_greater_eq { filter_eq { all_rows ; street address ; gold avenue sw } ; height ft / m ; 180 }, interpretation: select the rows whose street address record fuzzily matches to gold avenue sw . for the height ft / m records of these rows , all of them are greater than or equal to 180 .
Output: yes
Command: all_greater_eq { filter_eq { all_rows ; street address ; gold avenue sw } ; height ft / m ; 180 }, interpretation: select the rows whose us viewers ( in millions ) record is less than 8 . there is only one such row in the table . the no by series record of this unqiue row is 6 .
Output: no
Command: and { only { filter_less { all_rows ; us viewers ( in millions ) ; 8 } } ; eq { hop { filter_less { all_rows ; us viewers ( in millions ) ; 8 } ; no by series } ; 6 } }, interpretation: select the rows whose us viewers ( in millions ) record is less than 8 . there is only one such row in the table . the no by series record of this unqiue row is 6 .
Output: yes
Command: and { only { filter_less { all_rows ; us viewers ( in millions ) ; 8 } } ; eq { hop { filter_less { all_rows ; us viewers ( in millions ) ; 8 } ; no by series } ; 6 } }, interpretation: select the rows whose race time record is less than 3:00:00 . the number of such rows is 4 .
Output: no
Command: eq { count { filter_less { all_rows ; race time ; 3:00:00 } } ; 4 }, interpretation: select the rows whose race time record is less than 3:00:00 . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_less { all_rows ; race time ; 3:00:00 } } ; 4 }, interpretation: select the rows whose visitor record does not match to philadelphia . the number of such rows is 7 .
Output: no
Command: eq { count { filter_not_eq { all_rows ; visitor ; philadelphia } } ; 7 }, interpretation: select the rows whose visitor record does not match to philadelphia . the number of such rows is 7 .
Output: yes
Command: eq { count { filter_not_eq { all_rows ; visitor ; philadelphia } } ; 7 }, interpretation: select the rows whose year record is equal to 2005 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; year ; 2005 } } ; 2 }, interpretation: select the rows whose year record is equal to 2005 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; year ; 2005 } } ; 2 }, interpretation: select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose to par record is equal to -6 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; -6 } } ; 2 }, interpretation: select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose to par record is equal to -6 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; -6 } } ; 2 }, interpretation: for the enrollment ( 2010 ) records of all rows , all of them are less than 1000 .
Output: no
Command: all_less { all_rows ; enrollment ( 2010 ) ; 1000 }, interpretation: for the enrollment ( 2010 ) records of all rows , all of them are less than 1000 .
Output: yes
Command: all_less { all_rows ; enrollment ( 2010 ) ; 1000 }, interpretation: for the points records of all rows , most of them are greater than or equal to 3 .
Output: no
Command: most_greater_eq { all_rows ; points ; 3 }, interpretation: for the points records of all rows , most of them are greater than or equal to 3 .
Output: yes
Command: most_greater_eq { all_rows ; points ; 3 }, interpretation: select the rows whose podiums record is greater than 9 . there is only one such row in the table . the season record of this unqiue row is 2008 .
Output: no
Command: and { only { filter_greater { all_rows ; podiums ; 9 } } ; eq { hop { filter_greater { all_rows ; podiums ; 9 } ; season } ; 2008 } }, interpretation: select the rows whose podiums record is greater than 9 . there is only one such row in the table . the season record of this unqiue row is 2008 .
Output: yes
Command: and { only { filter_greater { all_rows ; podiums ; 9 } } ; eq { hop { filter_greater { all_rows ; podiums ; 9 } ; season } ; 2008 } }, interpretation: select the rows whose manner of departure record fuzzily matches to contract terminated . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; manner of departure ; contract terminated } } ; 3 }, interpretation: select the rows whose manner of departure record fuzzily matches to contract terminated . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; manner of departure ; contract terminated } } ; 3 }, interpretation: select the rows whose winner record fuzzily matches to luke youlden . there is only one such row in the table . the race title record of this unqiue row is mallala .
Output: no
Command: and { only { filter_eq { all_rows ; winner ; luke youlden } } ; eq { hop { filter_eq { all_rows ; winner ; luke youlden } ; race title } ; mallala } }, interpretation: select the rows whose winner record fuzzily matches to luke youlden . there is only one such row in the table . the race title record of this unqiue row is mallala .
Output: yes
Command: and { only { filter_eq { all_rows ; winner ; luke youlden } } ; eq { hop { filter_eq { all_rows ; winner ; luke youlden } ; race title } ; mallala } }, interpretation: select the rows whose name record fuzzily matches to uchana kalan . take the number of electorates ( 2009 ) record of this row . select the rows whose name record fuzzily matches to bawani khera . take the number of electorates ( 2009 ) record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; name ; uchana kalan } ; number of electorates ( 2009 ) } ; hop { filter_eq { all_rows ; name ; bawani khera } ; number of electorates ( 2009 ) } }, interpretation: select the rows whose name record fuzzily matches to uchana kalan . take the number of electorates ( 2009 ) record of this row . select the rows whose name record fuzzily matches to bawani khera . take the number of electorates ( 2009 ) record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; name ; uchana kalan } ; number of electorates ( 2009 ) } ; hop { filter_eq { all_rows ; name ; bawani khera } ; number of electorates ( 2009 ) } }, interpretation: for the date records of all rows , all of them fuzzily match to may .
Output: no
Command: all_eq { all_rows ; date ; may }, interpretation: for the date records of all rows , all of them fuzzily match to may .
Output: yes
Command: all_eq { all_rows ; date ; may }, interpretation: select the row whose years for grizzlies record of all rows is minimum . the player record of this row is eric leckner .
Output: no
Command: eq { hop { argmin { all_rows ; years for grizzlies } ; player } ; eric leckner }, interpretation: select the row whose years for grizzlies record of all rows is minimum . the player record of this row is eric leckner .
Output: yes
Command: eq { hop { argmin { all_rows ; years for grizzlies } ; player } ; eric leckner }, interpretation: select the rows whose l record is equal to 0 . there is only one such row in the table . the skip record of this unqiue row is kevin martin .
Output: no
Command: and { only { filter_eq { all_rows ; l ; 0 } } ; eq { hop { filter_eq { all_rows ; l ; 0 } ; skip } ; kevin martin } }, interpretation: select the rows whose l record is equal to 0 . there is only one such row in the table . the skip record of this unqiue row is kevin martin .
Output: yes
Command: and { only { filter_eq { all_rows ; l ; 0 } } ; eq { hop { filter_eq { all_rows ; l ; 0 } ; skip } ; kevin martin } }, interpretation: select the rows whose result record fuzzily matches to 2nd . there is only one such row in the table . the year record of this unqiue row is 1963 .
Output: no
Command: and { only { filter_eq { all_rows ; result ; 2nd } } ; eq { hop { filter_eq { all_rows ; result ; 2nd } ; year } ; 1963 } }, interpretation: select the rows whose result record fuzzily matches to 2nd . there is only one such row in the table . the year record of this unqiue row is 1963 .
Output: yes
Command: and { only { filter_eq { all_rows ; result ; 2nd } } ; eq { hop { filter_eq { all_rows ; result ; 2nd } ; year } ; 1963 } }, interpretation: select the rows whose club record fuzzily matches to real oviedo . take the wins record of this row . select the rows whose club record fuzzily matches to ca osasuna . take the wins record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; club ; real oviedo } ; wins } ; hop { filter_eq { all_rows ; club ; ca osasuna } ; wins } }, interpretation: select the rows whose club record fuzzily matches to real oviedo . take the wins record of this row . select the rows whose club record fuzzily matches to ca osasuna . take the wins record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; club ; real oviedo } ; wins } ; hop { filter_eq { all_rows ; club ; ca osasuna } ; wins } }, interpretation: select the row whose 13.5 - inch / 1400lb record of all rows is maximum . the ship record of this row is könig .
Output: no
Command: eq { hop { argmax { all_rows ; 13.5 - inch / 1400lb } ; ship } ; könig }, interpretation: select the row whose 13.5 - inch / 1400lb record of all rows is maximum . the ship record of this row is könig .
Output: yes
Command: eq { hop { argmax { all_rows ; 13.5 - inch / 1400lb } ; ship } ; könig }, interpretation: select the rows whose result record fuzzily matches to win . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; result ; win } } ; 4 }, interpretation: select the rows whose result record fuzzily matches to win . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; result ; win } } ; 4 }, interpretation: select the row whose rank record of all rows is 4th minimum . the rowers record of this row is zsolt hirling , tamã ¡ s varga .
Output: no
Command: eq { hop { nth_argmin { all_rows ; rank ; 4 } ; rowers } ; zsolt hirling , tamã ¡ s varga }, interpretation: select the row whose rank record of all rows is 4th minimum . the rowers record of this row is zsolt hirling , tamã ¡ s varga .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; rank ; 4 } ; rowers } ; zsolt hirling , tamã ¡ s varga }, interpretation: select the rows whose location record fuzzily matches to china . among these rows , select the rows whose authors record fuzzily matches to yuan . there is only one such row in the table . the name record of this unqiue row is didactylornis .
Output: no
Command: and { only { filter_eq { filter_eq { all_rows ; location ; china } ; authors ; yuan } } ; eq { hop { filter_eq { filter_eq { all_rows ; location ; china } ; authors ; yuan } ; name } ; didactylornis } }, interpretation: select the rows whose location record fuzzily matches to china . among these rows , select the rows whose authors record fuzzily matches to yuan . there is only one such row in the table . the name record of this unqiue row is didactylornis .
Output: yes
Command: and { only { filter_eq { filter_eq { all_rows ; location ; china } ; authors ; yuan } } ; eq { hop { filter_eq { filter_eq { all_rows ; location ; china } ; authors ; yuan } ; name } ; didactylornis } }, interpretation: select the rows whose athlete record fuzzily matches to tony geal . there is only one such row in the table .
Output: no
Command: only { filter_eq { all_rows ; athlete ; tony geal } }, interpretation: select the rows whose athlete record fuzzily matches to tony geal . there is only one such row in the table .
Output: yes
Command: only { filter_eq { all_rows ; athlete ; tony geal } }, interpretation: select the row whose opening week nett gross record of all rows is maximum . the movie record of this row is chennai express .
Output: no
Command: eq { hop { argmax { all_rows ; opening week nett gross } ; movie } ; chennai express }, interpretation: select the row whose opening week nett gross record of all rows is maximum . the movie record of this row is chennai express .
Output: yes
Command: eq { hop { argmax { all_rows ; opening week nett gross } ; movie } ; chennai express }, interpretation: select the rows whose number of contestants record is equal to 11 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; number of contestants ; 11 } } ; 3 }, interpretation: select the rows whose number of contestants record is equal to 11 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; number of contestants ; 11 } } ; 3 }, interpretation: for the result records of all rows , most of them fuzzily match to won .
Output: no
Command: most_eq { all_rows ; result ; won }, interpretation: select the rows whose country record fuzzily matches to usa . take the % of world demand record of this row . select the rows whose country record fuzzily matches to south korea . take the % of world demand record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; country ; usa } ; % of world demand } ; hop { filter_eq { all_rows ; country ; south korea } ; % of world demand } }, interpretation: select the rows whose country record fuzzily matches to usa . take the % of world demand record of this row . select the rows whose country record fuzzily matches to south korea . take the % of world demand record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; country ; usa } ; % of world demand } ; hop { filter_eq { all_rows ; country ; south korea } ; % of world demand } }, interpretation: select the row whose runners - up record of all rows is 2nd maximum . the clubs record of this row is primeiro de agosto .
Output: no
Command: eq { hop { nth_argmax { all_rows ; runners - up ; 2 } ; clubs } ; primeiro de agosto }, interpretation: select the row whose runners - up record of all rows is 2nd maximum . the clubs record of this row is primeiro de agosto .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; runners - up ; 2 } ; clubs } ; primeiro de agosto }, interpretation: select the row whose tonnes record of all rows is 2nd maximum . the location record of this row is anchorage , alaska .
Output: no
Command: eq { hop { nth_argmax { all_rows ; tonnes ; 2 } ; location } ; anchorage , alaska }, interpretation: select the row whose tonnes record of all rows is 2nd maximum . the location record of this row is anchorage , alaska .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; tonnes ; 2 } ; location } ; anchorage , alaska }, interpretation: select the rows whose player record fuzzily matches to ty warren . take the round record of this row . select the rows whose player record fuzzily matches to dan klecko . 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 ; player ; ty warren } ; round } ; hop { filter_eq { all_rows ; player ; dan klecko } ; round } }, interpretation: select the rows whose player record fuzzily matches to ty warren . take the round record of this row . select the rows whose player record fuzzily matches to dan klecko . 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 ; player ; ty warren } ; round } ; hop { filter_eq { all_rows ; player ; dan klecko } ; round } }, interpretation: select the rows whose outcome record fuzzily matches to winner . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; outcome ; winner } } ; 2 }, interpretation: select the rows whose outcome record fuzzily matches to winner . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; outcome ; winner } } ; 2 }, interpretation: for the outcome records of all rows , most of them fuzzily match to winner .
Output: no
Command: most_eq { all_rows ; outcome ; winner }, interpretation: for the location : records of all rows , most of them fuzzily match to nashville , united states .
Output: no
Command: most_eq { all_rows ; location : ; nashville , united states }, interpretation: for the location : records of all rows , most of them fuzzily match to nashville , united states .
Output: yes
Command: most_eq { all_rows ; location : ; nashville , united states }, interpretation: select the rows whose years record fuzzily matches to k - 6 . among these rows , select the rows whose suburb / town record fuzzily matches to villawood . there is only one such row in the table . the founded record of this unqiue row is 1955 .
Output: no