niryuu/sni-each-converted huggingface dataset:
Command: eq { hop { argmax { all_rows ; votes } ; election } ; 1990 }, interpretation: select the row whose votes record of all rows is maximum . the election record of this row is 1990 .
Output: yes
Command: eq { hop { argmax { all_rows ; votes } ; election } ; 1990 }, interpretation: select the rows whose result record fuzzily matches to win . for the orangemen points records of these rows , most of them are greater than 20 .
Output: no
Command: most_greater { filter_eq { all_rows ; result ; win } ; orangemen points ; 20 }, interpretation: select the rows whose result record fuzzily matches to win . for the orangemen points records of these rows , most of them are greater than 20 .
Output: yes
Command: most_greater { filter_eq { all_rows ; result ; win } ; orangemen points ; 20 }, interpretation: select the rows whose points defending record is equal to 0 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; points defending ; 0 } } ; 3 }, interpretation: select the rows whose points defending record is equal to 0 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; points defending ; 0 } } ; 3 }, interpretation: select the rows whose opponent record fuzzily matches to lee hasdell . take the round record of this row . select the rows whose opponent record fuzzily matches to zabir elbiev . take the round record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; opponent ; lee hasdell } ; round } ; hop { filter_eq { all_rows ; opponent ; zabir elbiev } ; round } }, interpretation: select the rows whose opponent record fuzzily matches to lee hasdell . take the round record of this row . select the rows whose opponent record fuzzily matches to zabir elbiev . take the round record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; opponent ; lee hasdell } ; round } ; hop { filter_eq { all_rows ; opponent ; zabir elbiev } ; round } }, interpretation: select the row whose crowd record of all rows is maximum . the venue record of this row is punt road oval .
Output: no
Command: eq { hop { argmax { all_rows ; crowd } ; venue } ; punt road oval }, interpretation: select the row whose crowd record of all rows is maximum . the venue record of this row is punt road oval .
Output: yes
Command: eq { hop { argmax { all_rows ; crowd } ; venue } ; punt road oval }, interpretation: select the rows whose surface record fuzzily matches to grass . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; surface ; grass } } ; 2 }, interpretation: select the rows whose surface record fuzzily matches to grass . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; surface ; grass } } ; 2 }, interpretation: select the row whose gdp per capita ( us ) record of all rows is maximum . the member countries record of this row is austria .
Output: no
Command: eq { hop { argmax { all_rows ; gdp per capita ( us ) } ; member countries } ; austria }, interpretation: select the row whose gdp per capita ( us ) record of all rows is maximum . the member countries record of this row is austria .
Output: yes
Command: eq { hop { argmax { all_rows ; gdp per capita ( us ) } ; member countries } ; austria }, interpretation: the average of the gold record of all rows is 1 .
Output: no
Command: round_eq { avg { all_rows ; gold } ; 1 }, interpretation: the average of the gold record of all rows is 1 .
Output: yes
Command: round_eq { avg { all_rows ; gold } ; 1 }, interpretation: select the row whose win % record of all rows is 1st maximum . the opposition record of this row is pune warriors india .
Output: no
Command: eq { hop { nth_argmax { all_rows ; win % ; 1 } ; opposition } ; pune warriors india }, interpretation: select the row whose win % record of all rows is 1st maximum . the opposition record of this row is pune warriors india .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; win % ; 1 } ; opposition } ; pune warriors india }, interpretation: for the format records of all rows , most of them fuzzily match to cd .
Output: no
Command: most_eq { all_rows ; format ; cd }, interpretation: for the format records of all rows , most of them fuzzily match to cd .
Output: yes
Command: most_eq { all_rows ; format ; cd }, interpretation: the average of the location attendance record of all rows is 14040 .
Output: no
Command: round_eq { avg { all_rows ; location attendance } ; 14040 }, interpretation: the average of the location attendance record of all rows is 14040 .
Output: yes
Command: round_eq { avg { all_rows ; location attendance } ; 14040 }, interpretation: the average of the win % record of all rows is 505 .
Output: no
Command: round_eq { avg { all_rows ; win % } ; 505 }, interpretation: the average of the win % record of all rows is 505 .
Output: yes
Command: round_eq { avg { all_rows ; win % } ; 505 }, interpretation: select the rows whose episodes record is less than 40 . there is only one such row in the table . the season no record of this unqiue row is 1 .
Output: no
Command: and { only { filter_less { all_rows ; episodes ; 40 } } ; eq { hop { filter_less { all_rows ; episodes ; 40 } ; season no } ; 1 } }, interpretation: select the rows whose episodes record is less than 40 . there is only one such row in the table . the season no record of this unqiue row is 1 .
Output: yes
Command: and { only { filter_less { all_rows ; episodes ; 40 } } ; eq { hop { filter_less { all_rows ; episodes ; 40 } ; season no } ; 1 } }, interpretation: select the rows whose country of origin record fuzzily matches to united kingdom . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; country of origin ; united kingdom } } ; 4 }, interpretation: select the rows whose country of origin record fuzzily matches to united kingdom . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; country of origin ; united kingdom } } ; 4 }, interpretation: select the rows whose opponent record fuzzily matches to lens . select the row whose date record of these rows is 2nd minimum . the venue record of this row is h .
Output: no
Command: eq { hop { nth_argmin { filter_eq { all_rows ; opponent ; lens } ; date ; 2 } ; venue } ; h }, interpretation: select the rows whose opponent record fuzzily matches to lens . select the row whose date record of these rows is 2nd minimum . the venue record of this row is h .
Output: yes
Command: eq { hop { nth_argmin { filter_eq { all_rows ; opponent ; lens } ; date ; 2 } ; venue } ; h }, interpretation: select the rows whose film title used in nomination record fuzzily matches to ten canoes . take the year ( ceremony ) record of this row . select the rows whose film title used in nomination record fuzzily matches to the home song stories . take the year ( ceremony ) record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; film title used in nomination ; ten canoes } ; year ( ceremony ) } ; hop { filter_eq { all_rows ; film title used in nomination ; the home song stories } ; year ( ceremony ) } }, interpretation: select the rows whose film title used in nomination record fuzzily matches to ten canoes . take the year ( ceremony ) record of this row . select the rows whose film title used in nomination record fuzzily matches to the home song stories . take the year ( ceremony ) record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; film title used in nomination ; ten canoes } ; year ( ceremony ) } ; hop { filter_eq { all_rows ; film title used in nomination ; the home song stories } ; year ( ceremony ) } }, interpretation: for the high points records of all rows , most of them fuzzily match to lebron james .
Output: no
Command: most_eq { all_rows ; high points ; lebron james }, interpretation: for the high points records of all rows , most of them fuzzily match to lebron james .
Output: yes
Command: most_eq { all_rows ; high points ; lebron james }, interpretation: select the rows whose bubbles record does not match to yes . there is only one such row in the table . the type record of this unqiue row is domnoderemovedfromdocument .
Output: no
Command: and { only { filter_not_eq { all_rows ; bubbles ; yes } } ; eq { hop { filter_not_eq { all_rows ; bubbles ; yes } ; type } ; domnoderemovedfromdocument } }, interpretation: select the rows whose bubbles record does not match to yes . there is only one such row in the table . the type record of this unqiue row is domnoderemovedfromdocument .
Output: yes
Command: and { only { filter_not_eq { all_rows ; bubbles ; yes } } ; eq { hop { filter_not_eq { all_rows ; bubbles ; yes } ; type } ; domnoderemovedfromdocument } }, interpretation: select the rows whose country record fuzzily matches to norway . among these rows , select the rows whose state record fuzzily matches to finnmark . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; country ; norway } ; state ; finnmark } } ; 3 }, interpretation: select the rows whose country record fuzzily matches to norway . among these rows , select the rows whose state record fuzzily matches to finnmark . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; country ; norway } ; state ; finnmark } } ; 3 }, interpretation: select the rows whose to par record fuzzily matches to +5 . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; to par ; +5 } } ; 4 }, interpretation: select the rows whose to par record fuzzily matches to +5 . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; to par ; +5 } } ; 4 }, interpretation: select the rows whose points record is equal to 0 . there is only one such row in the table . the opponent record of this unqiue row is new jersey devils .
Output: no
Command: and { only { filter_eq { all_rows ; points ; 0 } } ; eq { hop { filter_eq { all_rows ; points ; 0 } ; opponent } ; new jersey devils } }, interpretation: select the rows whose points record is equal to 0 . there is only one such row in the table . the opponent record of this unqiue row is new jersey devils .
Output: yes
Command: and { only { filter_eq { all_rows ; points ; 0 } } ; eq { hop { filter_eq { all_rows ; points ; 0 } ; opponent } ; new jersey devils } }, interpretation: select the row whose to par record of all rows is minimum . the player record of this row is bill rogers .
Output: no
Command: eq { hop { argmin { all_rows ; to par } ; player } ; bill rogers }, interpretation: select the row whose to par record of all rows is minimum . the player record of this row is bill rogers .
Output: yes
Command: eq { hop { argmin { all_rows ; to par } ; player } ; bill rogers }, interpretation: select the row whose goals record of all rows is minimum . the player record of this row is mehmet yılmaz .
Output: no
Command: eq { hop { argmin { all_rows ; goals } ; player } ; mehmet yılmaz }, interpretation: select the row whose goals record of all rows is minimum . the player record of this row is mehmet yılmaz .
Output: yes
Command: eq { hop { argmin { all_rows ; goals } ; player } ; mehmet yılmaz }, interpretation: select the rows whose competition record fuzzily matches to 2004 copa américa . there is only one such row in the table . the date record of this unqiue row is 14 july 2004 .
Output: no
Command: and { only { filter_eq { all_rows ; competition ; 2004 copa américa } } ; eq { hop { filter_eq { all_rows ; competition ; 2004 copa américa } ; date } ; 14 july 2004 } }, interpretation: select the rows whose competition record fuzzily matches to 2004 copa américa . there is only one such row in the table . the date record of this unqiue row is 14 july 2004 .
Output: yes
Command: and { only { filter_eq { all_rows ; competition ; 2004 copa américa } } ; eq { hop { filter_eq { all_rows ; competition ; 2004 copa américa } ; date } ; 14 july 2004 } }, interpretation: select the row whose crowd record of all rows is maximum . the venue record of this row is moorabbin oval .
Output: no
Command: eq { hop { argmax { all_rows ; crowd } ; venue } ; moorabbin oval }, interpretation: select the row whose crowd record of all rows is maximum . the venue record of this row is moorabbin oval .
Output: yes
Command: eq { hop { argmax { all_rows ; crowd } ; venue } ; moorabbin oval }, interpretation: for the affiliation records of all rows , most of them fuzzily match to private .
Output: no
Command: most_eq { all_rows ; affiliation ; private }, interpretation: for the affiliation records of all rows , most of them fuzzily match to private .
Output: yes
Command: most_eq { all_rows ; affiliation ; private }, interpretation: select the rows whose result record is less than 20.3 . the number of such rows is 7 .
Output: no
Command: eq { count { filter_less { all_rows ; result ; 20.3 } } ; 7 }, interpretation: select the rows whose result record is less than 20.3 . the number of such rows is 7 .
Output: yes
Command: eq { count { filter_less { all_rows ; result ; 20.3 } } ; 7 }, interpretation: select the rows whose opponent record fuzzily matches to chicago blackhawks . there is only one such row in the table . the game record of this unqiue row is 36 .
Output: no
Command: and { only { filter_eq { all_rows ; opponent ; chicago blackhawks } } ; eq { hop { filter_eq { all_rows ; opponent ; chicago blackhawks } ; game } ; 36 } }, interpretation: select the rows whose opponent record fuzzily matches to chicago blackhawks . there is only one such row in the table . the game record of this unqiue row is 36 .
Output: yes
Command: and { only { filter_eq { all_rows ; opponent ; chicago blackhawks } } ; eq { hop { filter_eq { all_rows ; opponent ; chicago blackhawks } ; game } ; 36 } }, interpretation: for the country records of all rows , most of them fuzzily match to united kingdom .
Output: no
Command: most_eq { all_rows ; country ; united kingdom }, interpretation: for the country records of all rows , most of them fuzzily match to united kingdom .
Output: yes
Command: most_eq { all_rows ; country ; united kingdom }, interpretation: the average of the bronze record of all rows is 1.7 .
Output: no
Command: round_eq { avg { all_rows ; bronze } ; 1.7 }, interpretation: the average of the bronze record of all rows is 1.7 .
Output: yes
Command: round_eq { avg { all_rows ; bronze } ; 1.7 }, interpretation: select the row whose population ( 2013 ) record of all rows is 2nd maximum . the province record of this row is kwazulu - natal .
Output: no
Command: eq { hop { nth_argmax { all_rows ; population ( 2013 ) ; 2 } ; province } ; kwazulu - natal }, interpretation: select the row whose population ( 2013 ) record of all rows is 2nd maximum . the province record of this row is kwazulu - natal .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; population ( 2013 ) ; 2 } ; province } ; kwazulu - natal }, interpretation: for the laps records of all rows , most of them are equal to 48 .
Output: no
Command: most_eq { all_rows ; laps ; 48 }, interpretation: for the laps records of all rows , most of them are equal to 48 .
Output: yes
Command: most_eq { all_rows ; laps ; 48 }, interpretation: select the row whose march record of all rows is 12th minimum . the score record of this row is 8 - 7 .
Output: no
Command: eq { hop { nth_argmin { all_rows ; march ; 12 } ; score } ; 8 - 7 }, interpretation: select the row whose march record of all rows is 12th minimum . the score record of this row is 8 - 7 .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; march ; 12 } ; score } ; 8 - 7 }, interpretation: select the rows whose director record fuzzily matches to hassard short . there is only one such row in the table . the date record of this unqiue row is june 3 , 1931 .
Output: no
Command: and { only { filter_eq { all_rows ; director ; hassard short } } ; eq { hop { filter_eq { all_rows ; director ; hassard short } ; date } ; june 3 , 1931 } }, interpretation: select the rows whose director record fuzzily matches to hassard short . there is only one such row in the table . the date record of this unqiue row is june 3 , 1931 .
Output: yes
Command: and { only { filter_eq { all_rows ; director ; hassard short } } ; eq { hop { filter_eq { all_rows ; director ; hassard short } ; date } ; june 3 , 1931 } }, interpretation: select the rows whose shirt sponsor record fuzzily matches to mardan . there is only one such row in the table . the team record of this unqiue row is antalyaspor .
Output: no
Command: and { only { filter_eq { all_rows ; shirt sponsor ; mardan } } ; eq { hop { filter_eq { all_rows ; shirt sponsor ; mardan } ; team } ; antalyaspor } }, interpretation: select the rows whose shirt sponsor record fuzzily matches to mardan . there is only one such row in the table . the team record of this unqiue row is antalyaspor .
Output: yes
Command: and { only { filter_eq { all_rows ; shirt sponsor ; mardan } } ; eq { hop { filter_eq { all_rows ; shirt sponsor ; mardan } ; team } ; antalyaspor } }, interpretation: the average of the assists record of all rows is 12.8 .
Output: no
Command: round_eq { avg { all_rows ; assists } ; 12.8 }, interpretation: the average of the assists record of all rows is 12.8 .
Output: yes
Command: round_eq { avg { all_rows ; assists } ; 12.8 }, interpretation: select the rows whose date of vacancy record fuzzily matches to 21 december 2010 . there is only one such row in the table . the outgoing head coach record of this unqiue row is jorge costa .
Output: no
Command: and { only { filter_eq { all_rows ; date of vacancy ; 21 december 2010 } } ; eq { hop { filter_eq { all_rows ; date of vacancy ; 21 december 2010 } ; outgoing head coach } ; jorge costa } }, interpretation: select the rows whose date of vacancy record fuzzily matches to 21 december 2010 . there is only one such row in the table . the outgoing head coach record of this unqiue row is jorge costa .
Output: yes
Command: and { only { filter_eq { all_rows ; date of vacancy ; 21 december 2010 } } ; eq { hop { filter_eq { all_rows ; date of vacancy ; 21 december 2010 } ; outgoing head coach } ; jorge costa } }, interpretation: select the row whose attendance record of all rows is 2nd maximum . the date record of this row is may 22 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; may 22 }, interpretation: select the row whose attendance record of all rows is 2nd maximum . the date record of this row is may 22 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; may 22 }, interpretation: the average of the home team score record of all rows is 10 .
Output: no
Command: round_eq { avg { all_rows ; home team score } ; 10 }, interpretation: the average of the home team score record of all rows is 10 .
Output: yes
Command: round_eq { avg { all_rows ; home team score } ; 10 }, interpretation: select the row whose us viewers ( million ) record of all rows is maximum . the title record of this row is exile on main st .
Output: no
Command: eq { hop { argmax { all_rows ; us viewers ( million ) } ; title } ; exile on main st }, interpretation: select the row whose us viewers ( million ) record of all rows is maximum . the title record of this row is exile on main st .
Output: yes
Command: eq { hop { argmax { all_rows ; us viewers ( million ) } ; title } ; exile on main st }, interpretation: for the result records of all rows , most of them fuzzily match to l .
Output: no
Command: most_eq { all_rows ; result ; l }, interpretation: select the rows whose tournament record fuzzily matches to tunis , tunisia . take the date record of this row . select the rows whose tournament record fuzzily matches to rabat , morocco . take the date record of this row . the second record is 7 days larger than the first record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; tournament ; tunis , tunisia } ; date } ; hop { filter_eq { all_rows ; tournament ; rabat , morocco } ; date } } ; -7 days }, interpretation: select the rows whose tournament record fuzzily matches to tunis , tunisia . take the date record of this row . select the rows whose tournament record fuzzily matches to rabat , morocco . take the date record of this row . the second record is 7 days larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; tournament ; tunis , tunisia } ; date } ; hop { filter_eq { all_rows ; tournament ; rabat , morocco } ; date } } ; -7 days }, interpretation: select the rows whose high assists record fuzzily matches to damon stoudamire . the sum of the high assists record of these rows is 95 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; high assists ; damon stoudamire } ; high assists } ; 95 }, interpretation: select the rows whose high assists record fuzzily matches to damon stoudamire . the sum of the high assists record of these rows is 95 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; high assists ; damon stoudamire } ; high assists } ; 95 }, 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: for the result records of all rows , most of them fuzzily match to won .
Output: yes
Command: most_eq { all_rows ; result ; won }, interpretation: for the pixel aspect ratio records of all rows , most of them fuzzily match to 1:1 .
Output: no
Command: most_eq { all_rows ; pixel aspect ratio ; 1:1 }, interpretation: for the pixel aspect ratio records of all rows , most of them fuzzily match to 1:1 .
Output: yes
Command: most_eq { all_rows ; pixel aspect ratio ; 1:1 }, interpretation: for the win % records of all rows , most of them are greater than 0 .
Output: no
Command: most_greater { all_rows ; win % ; 0 }, interpretation: for the win % records of all rows , most of them are greater than 0 .
Output: yes
Command: most_greater { all_rows ; win % ; 0 }, interpretation: the average of the quantity made record of all rows is 3.83 .
Output: no
Command: round_eq { avg { all_rows ; quantity made } ; 3.83 }, interpretation: the average of the quantity made record of all rows is 3.83 .
Output: yes
Command: round_eq { avg { all_rows ; quantity made } ; 3.83 }, interpretation: select the rows whose country record fuzzily matches to brazil . there is only one such row in the table . the player record of this unqiue row is angela park .
Output: no
Command: and { only { filter_eq { all_rows ; country ; brazil } } ; eq { hop { filter_eq { all_rows ; country ; brazil } ; player } ; angela park } }, interpretation: select the rows whose country record fuzzily matches to brazil . there is only one such row in the table . the player record of this unqiue row is angela park .
Output: yes
Command: and { only { filter_eq { all_rows ; country ; brazil } } ; eq { hop { filter_eq { all_rows ; country ; brazil } ; player } ; angela park } }, interpretation: the 2nd minimum year record of all rows is 1979 . the issued title record of the row with 2nd minimum year record is 1958 miles .
Output: no
Command: and { eq { nth_min { all_rows ; year ; 2 } ; 1979 } ; eq { hop { nth_argmin { all_rows ; year ; 2 } ; issued title } ; 1958 miles } }, interpretation: the 2nd minimum year record of all rows is 1979 . the issued title record of the row with 2nd minimum year record is 1958 miles .
Output: yes
Command: and { eq { nth_min { all_rows ; year ; 2 } ; 1979 } ; eq { hop { nth_argmin { all_rows ; year ; 2 } ; issued title } ; 1958 miles } }, interpretation: select the row whose total dismissals record of all rows is maximum . the no record of this row is 13 .
Output: no
Command: eq { hop { argmax { all_rows ; total dismissals } ; no } ; 13 }, interpretation: select the row whose total dismissals record of all rows is maximum . the no record of this row is 13 .
Output: yes
Command: eq { hop { argmax { all_rows ; total dismissals } ; no } ; 13 }, interpretation: the average of the attendance record of all rows is 58746 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 58746 }, interpretation: the average of the attendance record of all rows is 58746 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 58746 }, interpretation: select the rows whose notes record fuzzily matches to not published in book form . the number of such rows is 9 .
Output: no
Command: eq { count { filter_eq { all_rows ; notes ; not published in book form } } ; 9 }, interpretation: select the rows whose notes record fuzzily matches to not published in book form . the number of such rows is 9 .
Output: yes
Command: eq { count { filter_eq { all_rows ; notes ; not published in book form } } ; 9 }, interpretation: select the rows whose champions record fuzzily matches to laura montalvo . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; champions ; laura montalvo } } ; 3 }, interpretation: select the rows whose champions record fuzzily matches to laura montalvo . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; champions ; laura montalvo } } ; 3 }, interpretation: for the obama % records of all rows , most of them are greater than 50.0 % .
Output: no
Command: most_greater { all_rows ; obama % ; 50.0 % }, interpretation: for the obama % records of all rows , most of them are greater than 50.0 % .
Output: yes
Command: most_greater { all_rows ; obama % ; 50.0 % }, interpretation: select the rows whose winning team record fuzzily matches to houston . there is only one such row in the table . the date record of this unqiue row is june 24 .
Output: no
Command: and { only { filter_eq { all_rows ; winning team ; houston } } ; eq { hop { filter_eq { all_rows ; winning team ; houston } ; date } ; june 24 } }, interpretation: select the rows whose winning team record fuzzily matches to houston . there is only one such row in the table . the date record of this unqiue row is june 24 .
Output: yes
Command: and { only { filter_eq { all_rows ; winning team ; houston } } ; eq { hop { filter_eq { all_rows ; winning team ; houston } ; date } ; june 24 } }, interpretation: select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose total record is greater than or equal to 300 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_greater_eq { filter_eq { all_rows ; country ; united states } ; total ; 300 } } ; 3 }, interpretation: select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose total record is greater than or equal to 300 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_greater_eq { filter_eq { all_rows ; country ; united states } ; total ; 300 } } ; 3 }, interpretation: select the rows whose manufacturer record fuzzily matches to alco - schenectady . the minimum year made record of these rows is 1904 - 1907 .
Output: no
Command: eq { min { filter_eq { all_rows ; manufacturer ; alco - schenectady } ; year made } ; 1904 - 1907 }, interpretation: select the rows whose manufacturer record fuzzily matches to alco - schenectady . the minimum year made record of these rows is 1904 - 1907 .
Output: yes
Command: eq { min { filter_eq { all_rows ; manufacturer ; alco - schenectady } ; year made } ; 1904 - 1907 }, interpretation: select the rows whose publisher record fuzzily matches to bethesda softworks . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; publisher ; bethesda softworks } } ; 2 }, interpretation: select the rows whose publisher record fuzzily matches to bethesda softworks . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; publisher ; bethesda softworks } } ; 2 }, interpretation: select the row whose points record of all rows is 2nd maximum . the player record of this row is andy kaufmann .
Output: no
Command: eq { hop { nth_argmax { all_rows ; points ; 2 } ; player } ; andy kaufmann }, interpretation: select the row whose points record of all rows is 2nd maximum . the player record of this row is andy kaufmann .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; points ; 2 } ; player } ; andy kaufmann }, interpretation: select the row whose sites record of all rows is 2nd maximum . the circuit record of this row is amc entertainment inc .
Output: no
Command: eq { hop { nth_argmax { all_rows ; sites ; 2 } ; circuit } ; amc entertainment inc }, interpretation: select the row whose sites record of all rows is 2nd maximum . the circuit record of this row is amc entertainment inc .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; sites ; 2 } ; circuit } ; amc entertainment inc }, interpretation: select the row whose year record of all rows is 2nd maximum . the year record of this row is 2012 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; year ; 2 } ; year } ; 2012 }, interpretation: select the row whose year record of all rows is 2nd maximum . the year record of this row is 2012 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; year ; 2 } ; year } ; 2012 }, interpretation: select the rows whose object type record fuzzily matches to irregular galaxy . there is only one such row in the table . the ngc number record of this unqiue row is 6240 . the constellation record of this unqiue row is ophiuchus .
Output: no
Command: and { only { filter_eq { all_rows ; object type ; irregular galaxy } } ; and { eq { hop { filter_eq { all_rows ; object type ; irregular galaxy } ; ngc number } ; 6240 } ; eq { hop { filter_eq { all_rows ; object type ; irregular galaxy } ; constellation } ; ophiuchus } } }, interpretation: select the rows whose object type record fuzzily matches to irregular galaxy . there is only one such row in the table . the ngc number record of this unqiue row is 6240 . the constellation record of this unqiue row is ophiuchus .
Output: yes
Command: and { only { filter_eq { all_rows ; object type ; irregular galaxy } } ; and { eq { hop { filter_eq { all_rows ; object type ; irregular galaxy } ; ngc number } ; 6240 } ; eq { hop { filter_eq { all_rows ; object type ; irregular galaxy } ; constellation } ; ophiuchus } } }, interpretation: select the rows whose state record fuzzily matches to oregon . select the row whose year record of these rows is 2nd minimum . the result record of this row is resigned .
Output: no
Command: eq { hop { nth_argmin { filter_eq { all_rows ; state ; oregon } ; year ; 2 } ; result } ; resigned }, interpretation: select the rows whose state record fuzzily matches to oregon . select the row whose year record of these rows is 2nd minimum . the result record of this row is resigned .
Output: yes
Command: eq { hop { nth_argmin { filter_eq { all_rows ; state ; oregon } ; year ; 2 } ; result } ; resigned }, interpretation: select the rows whose club record fuzzily matches to sussex . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; club ; sussex } } ; 3 }, interpretation: select the rows whose club record fuzzily matches to sussex . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; club ; sussex } } ; 3 }, interpretation: select the rows whose score record fuzzily matches to 3 - 0 . take the competition record of this row . select the rows whose score record fuzzily matches to 3 - 2 . take the competition record of this row . the first record fuzzily matches to the second record . the competition record of the first row is friendly . the competition record of the second row is friendly .
Output: no
Command: and { eq { hop { filter_eq { all_rows ; score ; 3 - 0 } ; competition } ; hop { filter_eq { all_rows ; score ; 3 - 2 } ; competition } } ; and { eq { hop { filter_eq { all_rows ; score ; 3 - 0 } ; competition } ; friendly } ; eq { hop { filter_eq { all_rows ; score ; 3 - 2 } ; competition } ; friendly } } }, interpretation: select the rows whose score record fuzzily matches to 3 - 0 . take the competition record of this row . select the rows whose score record fuzzily matches to 3 - 2 . take the competition record of this row . the first record fuzzily matches to the second record . the competition record of the first row is friendly . the competition record of the second row is friendly .
Output: yes
Command: and { eq { hop { filter_eq { all_rows ; score ; 3 - 0 } ; competition } ; hop { filter_eq { all_rows ; score ; 3 - 2 } ; competition } } ; and { eq { hop { filter_eq { all_rows ; score ; 3 - 0 } ; competition } ; friendly } ; eq { hop { filter_eq { all_rows ; score ; 3 - 2 } ; competition } ; friendly } } }, interpretation: for the surface records of all rows , most of them fuzzily match to hard .
Output: no
Command: most_eq { all_rows ; surface ; hard }, interpretation: select the rows whose year record is equal to 1984 . the sum of the points record of these rows is 2 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; year ; 1984 } ; points } ; 2 }, interpretation: select the rows whose year record is equal to 1984 . the sum of the points record of these rows is 2 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; year ; 1984 } ; points } ; 2 }, interpretation: select the row whose episode record of all rows is 2nd maximum . the segment b record of this row is compact track loaders .
Output: no
Command: eq { hop { nth_argmax { all_rows ; episode ; 2 } ; segment b } ; compact track loaders }, interpretation: select the row whose episode record of all rows is 2nd maximum . the segment b record of this row is compact track loaders .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; episode ; 2 } ; segment b } ; compact track loaders }, interpretation: select the rows whose competition record fuzzily matches to world championships . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; competition ; world championships } } ; 3 }, interpretation: select the rows whose competition record fuzzily matches to world championships . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; competition ; world championships } } ; 3 }, interpretation: select the row whose new membership total record of all rows is maximum . the conference record of this row is hockey east ( men ) .
Output: no
Command: eq { hop { argmax { all_rows ; new membership total } ; conference } ; hockey east ( men ) }, interpretation: select the row whose new membership total record of all rows is maximum . the conference record of this row is hockey east ( men ) .
Output: yes
Command: eq { hop { argmax { all_rows ; new membership total } ; conference } ; hockey east ( men ) }, interpretation: select the rows whose bronze record is equal to 8 . there is only one such row in the table . the rank record of this unqiue row is 3 .
Output: no
Command: and { only { filter_eq { all_rows ; bronze ; 8 } } ; eq { hop { filter_eq { all_rows ; bronze ; 8 } ; rank } ; 3 } }, interpretation: select the rows whose bronze record is equal to 8 . there is only one such row in the table . the rank record of this unqiue row is 3 .
Output: yes
Command: and { only { filter_eq { all_rows ; bronze ; 8 } } ; eq { hop { filter_eq { all_rows ; bronze ; 8 } ; rank } ; 3 } }, interpretation: for the transfer fee ( millions ) records of all rows , most of them are greater than 26 .
Output: no
Command: most_greater { all_rows ; transfer fee ( millions ) ; 26 }, interpretation: for the transfer fee ( millions ) records of all rows , most of them are greater than 26 .
Output: yes
Command: most_greater { all_rows ; transfer fee ( millions ) ; 26 }, interpretation: for the result records of all rows , most of them fuzzily match to w .
Output: no
Command: most_eq { all_rows ; result ; w }, interpretation: for the result records of all rows , most of them fuzzily match to w .
Output: yes
Command: most_eq { all_rows ; result ; w }, interpretation: select the row whose county team record of all rows is 2nd minimum . the player record of this row is mick mackey .
Output: no
Command: eq { hop { nth_argmin { all_rows ; county team ; 2 } ; player } ; mick mackey }, interpretation: select the row whose county team record of all rows is 2nd minimum . the player record of this row is mick mackey .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; county team ; 2 } ; player } ; mick mackey }, interpretation: select the rows whose theatre record fuzzily matches to royale theatre . there is only one such row in the table . the role record of this unqiue row is geoffrey fitton .
Output: no
Command: and { only { filter_eq { all_rows ; theatre ; royale theatre } } ; eq { hop { filter_eq { all_rows ; theatre ; royale theatre } ; role } ; geoffrey fitton } }, interpretation: select the rows whose theatre record fuzzily matches to royale theatre . there is only one such row in the table . the role record of this unqiue row is geoffrey fitton .
Output: yes
Command: and { only { filter_eq { all_rows ; theatre ; royale theatre } } ; eq { hop { filter_eq { all_rows ; theatre ; royale theatre } ; role } ; geoffrey fitton } }, interpretation: select the rows whose event record fuzzily matches to ept barcelona open . take the date record of this row . select the rows whose event record fuzzily matches to 2008 european poker championships . take the date record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; event ; ept barcelona open } ; date } ; hop { filter_eq { all_rows ; event ; 2008 european poker championships } ; date } }, interpretation: select the rows whose event record fuzzily matches to ept barcelona open . take the date record of this row . select the rows whose event record fuzzily matches to 2008 european poker championships . take the date record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; event ; ept barcelona open } ; date } ; hop { filter_eq { all_rows ; event ; 2008 european poker championships } ; date } }, interpretation: the sum of the 1st prize record of all rows is 241700 .
Output: no
Command: round_eq { sum { all_rows ; 1st prize } ; 241700 }, interpretation: the sum of the 1st prize record of all rows is 241700 .
Output: yes
Command: round_eq { sum { all_rows ; 1st prize } ; 241700 }, interpretation: select the rows whose date successor seated record fuzzily matches to may . select the row whose date successor seated record of these rows is 1st minimum . the district record of this row is california 10th .
Output: no
Command: eq { hop { nth_argmin { filter_eq { all_rows ; date successor seated ; may } ; date successor seated ; 1 } ; district } ; california 10th }, interpretation: select the rows whose date successor seated record fuzzily matches to may . select the row whose date successor seated record of these rows is 1st minimum . the district record of this row is california 10th .
Output: yes
Command: eq { hop { nth_argmin { filter_eq { all_rows ; date successor seated ; may } ; date successor seated ; 1 } ; district } ; california 10th }, interpretation: select the rows whose score record fuzzily matches to w . among these rows , select the rows whose high assists record fuzzily matches to g payton . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; score ; w } ; high assists ; g payton } } ; 3 }, interpretation: select the rows whose score record fuzzily matches to w . among these rows , select the rows whose high assists record fuzzily matches to g payton . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; score ; w } ; high assists ; g payton } } ; 3 }, interpretation: select the rows whose station record fuzzily matches to great notch . take the closed record of this row . select the rows whose station record fuzzily matches to finderne . take the closed record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; station ; great notch } ; closed } ; hop { filter_eq { all_rows ; station ; finderne } ; closed } }, interpretation: select the rows whose station record fuzzily matches to great notch . take the closed record of this row . select the rows whose station record fuzzily matches to finderne . take the closed record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; station ; great notch } ; closed } ; hop { filter_eq { all_rows ; station ; finderne } ; closed } }, interpretation: select the row whose losses record of all rows is 2nd maximum . the club record of this row is castilla cf .
Output: no
Command: eq { hop { nth_argmax { all_rows ; losses ; 2 } ; club } ; castilla cf }, interpretation: select the row whose losses record of all rows is 2nd maximum . the club record of this row is castilla cf .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; losses ; 2 } ; club } ; castilla cf }, interpretation: select the rows whose time / retired record fuzzily matches to off course . there is only one such row in the table . the driver record of this unqiue row is gastón mazzacane .
Output: no
Command: and { only { filter_eq { all_rows ; time / retired ; off course } } ; eq { hop { filter_eq { all_rows ; time / retired ; off course } ; driver } ; gastón mazzacane } }, interpretation: select the rows whose time / retired record fuzzily matches to off course . there is only one such row in the table . the driver record of this unqiue row is gastón mazzacane .
Output: yes
Command: and { only { filter_eq { all_rows ; time / retired ; off course } } ; eq { hop { filter_eq { all_rows ; time / retired ; off course } ; driver } ; gastón mazzacane } }, interpretation: select the rows whose rider record fuzzily matches to stan woods . take the time record of this row . select the rows whose rider record fuzzily matches to tom loughridge . take the time record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; rider ; stan woods } ; time } ; hop { filter_eq { all_rows ; rider ; tom loughridge } ; time } }, interpretation: select the rows whose rider record fuzzily matches to stan woods . take the time record of this row . select the rows whose rider record fuzzily matches to tom loughridge . take the time record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; rider ; stan woods } ; time } ; hop { filter_eq { all_rows ; rider ; tom loughridge } ; time } }, interpretation: select the rows whose record record fuzzily matches to postponed ( rain ) . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; record ; postponed ( rain ) } } ; 2 }, interpretation: select the rows whose record record fuzzily matches to postponed ( rain ) . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; record ; postponed ( rain ) } } ; 2 }, interpretation: select the rows whose technology record fuzzily matches to rmi . the maximum date record of these rows is september 16 , 2009 .
Output: no
Command: eq { max { filter_eq { all_rows ; technology ; rmi } ; date } ; september 16 , 2009 }, interpretation: select the rows whose technology record fuzzily matches to rmi . the maximum date record of these rows is september 16 , 2009 .
Output: yes
Command: eq { max { filter_eq { all_rows ; technology ; rmi } ; date } ; september 16 , 2009 }, interpretation: select the rows whose apps record is equal to 12 . there is only one such row in the table . the season record of this unqiue row is 2004 - 2005 .
Output: no
Command: and { only { filter_eq { all_rows ; apps ; 12 } } ; eq { hop { filter_eq { all_rows ; apps ; 12 } ; season } ; 2004 - 2005 } }, interpretation: select the rows whose apps record is equal to 12 . there is only one such row in the table . the season record of this unqiue row is 2004 - 2005 .
Output: yes
Command: and { only { filter_eq { all_rows ; apps ; 12 } } ; eq { hop { filter_eq { all_rows ; apps ; 12 } ; season } ; 2004 - 2005 } }, interpretation: select the rows whose player record fuzzily matches to ( can ) . the sum of the goals record of these rows is 26 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; player ; ( can ) } ; goals } ; 26 }, interpretation: select the rows whose player record fuzzily matches to ( can ) . the sum of the goals record of these rows is 26 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; player ; ( can ) } ; goals } ; 26 }, interpretation: select the row whose matches record of all rows is 2nd maximum . the name record of this row is glenn mcgrath .
Output: no
Command: eq { hop { nth_argmax { all_rows ; matches ; 2 } ; name } ; glenn mcgrath }, interpretation: select the row whose matches record of all rows is 2nd maximum . the name record of this row is glenn mcgrath .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; matches ; 2 } ; name } ; glenn mcgrath }, interpretation: select the rows whose name of county record fuzzily matches to veszprém . take the area ( km square ) record of this row . select the rows whose name of county record fuzzily matches to tolna . take the area ( km square ) record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; name of county ; veszprém } ; area ( km square ) } ; hop { filter_eq { all_rows ; name of county ; tolna } ; area ( km square ) } }, interpretation: select the rows whose name of county record fuzzily matches to veszprém . take the area ( km square ) record of this row . select the rows whose name of county record fuzzily matches to tolna . take the area ( km square ) record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; name of county ; veszprém } ; area ( km square ) } ; hop { filter_eq { all_rows ; name of county ; tolna } ; area ( km square ) } }, interpretation: select the rows whose venue record fuzzily matches to windy hill . take the crowd record of this row . select the rows whose venue record fuzzily matches to princes park . take the crowd record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; venue ; windy hill } ; crowd } ; hop { filter_eq { all_rows ; venue ; princes park } ; crowd } }, interpretation: select the rows whose venue record fuzzily matches to windy hill . take the crowd record of this row . select the rows whose venue record fuzzily matches to princes park . take the crowd record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; venue ; windy hill } ; crowd } ; hop { filter_eq { all_rows ; venue ; princes park } ; crowd } }, interpretation: select the row whose result record of all rows is maximum . the opponent record of this row is st louis rams .
Output: no
Command: eq { hop { argmax { all_rows ; result } ; opponent } ; st louis rams }, interpretation: select the row whose result record of all rows is maximum . the opponent record of this row is st louis rams .
Output: yes
Command: eq { hop { argmax { all_rows ; result } ; opponent } ; st louis rams }, 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 row whose crowd record of all rows is 1st maximum . the venue record of this row is victoria park .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; victoria park }, interpretation: select the rows whose builder record fuzzily matches to north british . take the date record of this row . select the rows whose builder record fuzzily matches to g & swr kilmarnock . take the date record of this row . the first record is less than the second record .
Output: no