niryuu/sni-each-converted huggingface dataset:
Command: greater { hop { filter_eq { all_rows ; venue ; mcg } ; crowd } ; hop { filter_eq { all_rows ; venue ; vfl park } ; crowd } }, interpretation: select the rows whose venue record fuzzily matches to mcg . take the crowd record of this row . select the rows whose venue record fuzzily matches to vfl 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 ; mcg } ; crowd } ; hop { filter_eq { all_rows ; venue ; vfl park } ; crowd } }, interpretation: select the rows whose goals record is less than 55 . there is only one such row in the table . the player record of this unqiue row is eusébio .
Output: no
Command: and { only { filter_less { all_rows ; goals ; 55 } } ; eq { hop { filter_less { all_rows ; goals ; 55 } ; player } ; eusébio } }, interpretation: select the rows whose goals record is less than 55 . there is only one such row in the table . the player record of this unqiue row is eusébio .
Output: yes
Command: and { only { filter_less { all_rows ; goals ; 55 } } ; eq { hop { filter_less { all_rows ; goals ; 55 } ; player } ; eusébio } }, interpretation: select the rows whose home team record fuzzily matches to geelong . take the home team score record of this row . select the rows whose home team record fuzzily matches to richmond . take the home team score record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; home team ; geelong } ; home team score } ; hop { filter_eq { all_rows ; home team ; richmond } ; home team score } }, interpretation: select the row whose capacity record of all rows is 3rd maximum . the stadium record of this row is stadion gradski vrt .
Output: no
Command: eq { hop { nth_argmax { all_rows ; capacity ; 3 } ; stadium } ; stadion gradski vrt }, interpretation: select the row whose capacity record of all rows is 3rd maximum . the stadium record of this row is stadion gradski vrt .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; capacity ; 3 } ; stadium } ; stadion gradski vrt }, interpretation: select the rows whose year record fuzzily matches to 1965 . take the overall record record of this row . select the rows whose year record fuzzily matches to 1959 . take the overall record record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; year ; 1965 } ; overall record } ; hop { filter_eq { all_rows ; year ; 1959 } ; overall record } }, interpretation: select the rows whose year record fuzzily matches to 1965 . take the overall record record of this row . select the rows whose year record fuzzily matches to 1959 . take the overall record record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; year ; 1965 } ; overall record } ; hop { filter_eq { all_rows ; year ; 1959 } ; overall record } }, interpretation: select the row whose height record of all rows is 2nd maximum . the player record of this row is argo meresaar ( c ) .
Output: no
Command: eq { hop { nth_argmax { all_rows ; height ; 2 } ; player } ; argo meresaar ( c ) }, interpretation: select the row whose height record of all rows is 2nd maximum . the player record of this row is argo meresaar ( c ) .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; height ; 2 } ; player } ; argo meresaar ( c ) }, interpretation: select the rows whose successor record fuzzily matches to vacant . there is only one such row in the table . the district record of this unqiue row is illinois 1st .
Output: no
Command: and { only { filter_eq { all_rows ; successor ; vacant } } ; eq { hop { filter_eq { all_rows ; successor ; vacant } ; district } ; illinois 1st } }, interpretation: select the rows whose successor record fuzzily matches to vacant . there is only one such row in the table . the district record of this unqiue row is illinois 1st .
Output: yes
Command: and { only { filter_eq { all_rows ; successor ; vacant } } ; eq { hop { filter_eq { all_rows ; successor ; vacant } ; district } ; illinois 1st } }, interpretation: select the row whose w record of all rows is 2nd maximum . the team record of this row is narbonne .
Output: no
Command: eq { hop { nth_argmax { all_rows ; w ; 2 } ; team } ; narbonne }, interpretation: select the row whose w record of all rows is 2nd maximum . the team record of this row is narbonne .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; w ; 2 } ; team } ; narbonne }, interpretation: select the row whose against record of all rows is maximum . the opposing team record of this row is australia .
Output: no
Command: eq { hop { argmax { all_rows ; against } ; opposing team } ; australia }, interpretation: select the row whose against record of all rows is maximum . the opposing team record of this row is australia .
Output: yes
Command: eq { hop { argmax { all_rows ; against } ; opposing team } ; australia }, interpretation: select the row whose round record of all rows is minimum . the name record of this row is joseph addai .
Output: no
Command: eq { hop { argmin { all_rows ; round } ; name } ; joseph addai }, interpretation: select the row whose round record of all rows is minimum . the name record of this row is joseph addai .
Output: yes
Command: eq { hop { argmin { all_rows ; round } ; name } ; joseph addai }, interpretation: select the rows whose station record fuzzily matches to kstp - tv . there is only one such row in the table . the channel record of this unqiue row is 5.1 .
Output: no
Command: and { only { filter_eq { all_rows ; station ; kstp - tv } } ; eq { hop { filter_eq { all_rows ; station ; kstp - tv } ; channel } ; 5.1 } }, interpretation: select the rows whose station record fuzzily matches to kstp - tv . there is only one such row in the table . the channel record of this unqiue row is 5.1 .
Output: yes
Command: and { only { filter_eq { all_rows ; station ; kstp - tv } } ; eq { hop { filter_eq { all_rows ; station ; kstp - tv } ; channel } ; 5.1 } }, interpretation: select the rows whose resolution record fuzzily matches to committed suicide . there is only one such row in the table . the name record of this unqiue row is josé manuel balmaceda .
Output: no
Command: and { only { filter_eq { all_rows ; resolution ; committed suicide } } ; eq { hop { filter_eq { all_rows ; resolution ; committed suicide } ; name } ; josé manuel balmaceda } }, interpretation: select the rows whose resolution record fuzzily matches to committed suicide . there is only one such row in the table . the name record of this unqiue row is josé manuel balmaceda .
Output: yes
Command: and { only { filter_eq { all_rows ; resolution ; committed suicide } } ; eq { hop { filter_eq { all_rows ; resolution ; committed suicide } ; name } ; josé manuel balmaceda } }, interpretation: select the rows whose venue record is arbitrary . the number of such rows is 6 .
Output: no
Command: eq { count { filter_all { all_rows ; venue } } ; 6 }, interpretation: select the rows whose colors record fuzzily matches to white . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; colors ; white } } ; 3 }, interpretation: select the rows whose colors record fuzzily matches to white . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; colors ; white } } ; 3 }, interpretation: select the row whose date of vacancy record of all rows is minimum . the outgoing manager record of this row is mauricio cienfuegos .
Output: no
Command: eq { hop { argmin { all_rows ; date of vacancy } ; outgoing manager } ; mauricio cienfuegos }, interpretation: select the row whose date of vacancy record of all rows is minimum . the outgoing manager record of this row is mauricio cienfuegos .
Output: yes
Command: eq { hop { argmin { all_rows ; date of vacancy } ; outgoing manager } ; mauricio cienfuegos }, interpretation: the average of the total record of all rows is 32366 .
Output: no
Command: round_eq { avg { all_rows ; total } ; 32366 }, interpretation: the average of the total record of all rows is 32366 .
Output: yes
Command: round_eq { avg { all_rows ; total } ; 32366 }, interpretation: select the rows whose kitmaker record fuzzily matches to adidas . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; kitmaker ; adidas } } ; 3 }, interpretation: select the rows whose kitmaker record fuzzily matches to adidas . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; kitmaker ; adidas } } ; 3 }, interpretation: for the kitmaker records of all rows , most of them do not match to n / a .
Output: no
Command: most_not_eq { all_rows ; kitmaker ; n / a }, interpretation: for the kitmaker records of all rows , most of them do not match to n / a .
Output: yes
Command: most_not_eq { all_rows ; kitmaker ; n / a }, interpretation: select the rows whose steals record fuzzily matches to 3 tied . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; steals ; 3 tied } } ; 4 }, interpretation: select the rows whose steals record fuzzily matches to 3 tied . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; steals ; 3 tied } } ; 4 }, interpretation: select the rows whose gold record is greater than 200 . for the total records of these rows , all of them are greater than 1000 .
Output: no
Command: all_greater { filter_greater { all_rows ; gold ; 200 } ; total ; 1000 }, interpretation: select the rows whose gold record is greater than 200 . for the total records of these rows , all of them are greater than 1000 .
Output: yes
Command: all_greater { filter_greater { all_rows ; gold ; 200 } ; total ; 1000 }, interpretation: select the rows whose opponent in the final record fuzzily matches to ágnes szávay . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; opponent in the final ; ágnes szávay } } ; 2 }, interpretation: select the rows whose opponent in the final record fuzzily matches to ágnes szávay . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; opponent in the final ; ágnes szávay } } ; 2 }, interpretation: the average of the total passengers record of all rows is 4773780 .
Output: no
Command: round_eq { avg { all_rows ; total passengers } ; 4773780 }, interpretation: the average of the total passengers record of all rows is 4773780 .
Output: yes
Command: round_eq { avg { all_rows ; total passengers } ; 4773780 }, interpretation: the 2nd minimum rank record of all rows is 2 . the name record of the row with 2nd minimum rank record is camelia potec . the nationality record of the row with 2nd minimum rank record is romania . the time record of the row with 2nd minimum rank record is 1:59.54 .
Output: no
Command: and { eq { nth_min { all_rows ; rank ; 2 } ; 2 } ; and { eq { hop { nth_argmin { all_rows ; rank ; 2 } ; name } ; camelia potec } ; and { eq { hop { nth_argmin { all_rows ; rank ; 2 } ; nationality } ; romania } ; eq { hop { nth_argmin { all_rows ; rank ; 2 } ; time } ; 1:59.54 } } } }, interpretation: the 2nd minimum rank record of all rows is 2 . the name record of the row with 2nd minimum rank record is camelia potec . the nationality record of the row with 2nd minimum rank record is romania . the time record of the row with 2nd minimum rank record is 1:59.54 .
Output: yes
Command: and { eq { nth_min { all_rows ; rank ; 2 } ; 2 } ; and { eq { hop { nth_argmin { all_rows ; rank ; 2 } ; name } ; camelia potec } ; and { eq { hop { nth_argmin { all_rows ; rank ; 2 } ; nationality } ; romania } ; eq { hop { nth_argmin { all_rows ; rank ; 2 } ; time } ; 1:59.54 } } } }, interpretation: select the rows whose laps record is greater than 312 . there is only one such row in the table .
Output: no
Command: only { filter_greater { all_rows ; laps ; 312 } }, interpretation: select the rows whose laps record is greater than 312 . there is only one such row in the table .
Output: yes
Command: only { filter_greater { all_rows ; laps ; 312 } }, interpretation: select the row whose ratings record of all rows is maximum . the guest ( s ) record of this row is matt lucas .
Output: no
Command: eq { hop { argmax { all_rows ; ratings } ; guest ( s ) } ; matt lucas }, interpretation: select the row whose ratings record of all rows is maximum . the guest ( s ) record of this row is matt lucas .
Output: yes
Command: eq { hop { argmax { all_rows ; ratings } ; guest ( s ) } ; matt lucas }, interpretation: for the best records of all rows , most of them are less than 59.999 .
Output: no
Command: most_less { all_rows ; best ; 59.999 }, interpretation: for the best records of all rows , most of them are less than 59.999 .
Output: yes
Command: most_less { all_rows ; best ; 59.999 }, interpretation: select the row whose top 10 record of all rows is maximum . the year record of this row is 2005 .
Output: no
Command: eq { hop { argmax { all_rows ; top 10 } ; year } ; 2005 }, interpretation: select the row whose top 10 record of all rows is maximum . the year record of this row is 2005 .
Output: yes
Command: eq { hop { argmax { all_rows ; top 10 } ; year } ; 2005 }, interpretation: for the year named records of all rows , most of them are less than 1998 .
Output: no
Command: most_less { all_rows ; year named ; 1998 }, interpretation: for the year named records of all rows , most of them are less than 1998 .
Output: yes
Command: most_less { all_rows ; year named ; 1998 }, interpretation: select the rows whose max processors record fuzzily matches to 1 ultrasparc t1 . select the row whose max memory record of these rows is 1st minimum . the model record of this row is t1000 .
Output: no
Command: eq { hop { nth_argmin { filter_eq { all_rows ; max processors ; 1 ultrasparc t1 } ; max memory ; 1 } ; model } ; t1000 }, interpretation: select the rows whose max processors record fuzzily matches to 1 ultrasparc t1 . select the row whose max memory record of these rows is 1st minimum . the model record of this row is t1000 .
Output: yes
Command: eq { hop { nth_argmin { filter_eq { all_rows ; max processors ; 1 ultrasparc t1 } ; max memory ; 1 } ; model } ; t1000 }, interpretation: select the rows whose no in season record fuzzily matches to 1 / 2 . take the us viewers ( million ) record of this row . select the rows whose no in season record fuzzily matches to 16 . take the us viewers ( million ) record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; no in season ; 1 / 2 } ; us viewers ( million ) } ; hop { filter_eq { all_rows ; no in season ; 16 } ; us viewers ( million ) } }, interpretation: select the rows whose no in season record fuzzily matches to 1 / 2 . take the us viewers ( million ) record of this row . select the rows whose no in season record fuzzily matches to 16 . take the us viewers ( million ) record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; no in season ; 1 / 2 } ; us viewers ( million ) } ; hop { filter_eq { all_rows ; no in season ; 16 } ; us viewers ( million ) } }, interpretation: for the rhode island records of all rows , most of them fuzzily match to lincoln ll lincoln .
Output: no
Command: most_eq { all_rows ; rhode island ; lincoln ll lincoln }, interpretation: for the rhode island records of all rows , most of them fuzzily match to lincoln ll lincoln .
Output: yes
Command: most_eq { all_rows ; rhode island ; lincoln ll lincoln }, interpretation: select the rows whose visitor record fuzzily matches to los angeles . take the date record of this row . select the rows whose visitor record fuzzily matches to toronto . 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 ; visitor ; los angeles } ; date } ; hop { filter_eq { all_rows ; visitor ; toronto } ; date } }, interpretation: select the rows whose visitor record fuzzily matches to los angeles . take the date record of this row . select the rows whose visitor record fuzzily matches to toronto . 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 ; visitor ; los angeles } ; date } ; hop { filter_eq { all_rows ; visitor ; toronto } ; date } }, interpretation: select the row whose length record of all rows is 2nd maximum . the line name record of this row is north auckland line .
Output: no
Command: eq { hop { nth_argmax { all_rows ; length ; 2 } ; line name } ; north auckland line }, interpretation: select the row whose length record of all rows is 2nd maximum . the line name record of this row is north auckland line .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; length ; 2 } ; line name } ; north auckland line }, interpretation: select the rows whose director record fuzzily matches to benedict andrews . there is only one such row in the table . the production record of this unqiue row is the war of the roses .
Output: no
Command: and { only { filter_eq { all_rows ; director ; benedict andrews } } ; eq { hop { filter_eq { all_rows ; director ; benedict andrews } ; production } ; the war of the roses } }, interpretation: select the rows whose director record fuzzily matches to benedict andrews . there is only one such row in the table . the production record of this unqiue row is the war of the roses .
Output: yes
Command: and { only { filter_eq { all_rows ; director ; benedict andrews } } ; eq { hop { filter_eq { all_rows ; director ; benedict andrews } ; production } ; the war of the roses } }, interpretation: select the rows whose format record fuzzily matches to oldies . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; format ; oldies } } ; 4 }, interpretation: select the rows whose format record fuzzily matches to oldies . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; format ; oldies } } ; 4 }, interpretation: select the rows whose drawn record is equal to 1 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; drawn ; 1 } } ; 2 }, interpretation: select the rows whose drawn record is equal to 1 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; drawn ; 1 } } ; 2 }, interpretation: select the rows whose competing entities record fuzzily matches to individuals . the minimum first held record of these rows is 1931 .
Output: no
Command: eq { min { filter_eq { all_rows ; competing entities ; individuals } ; first held } ; 1931 }, interpretation: select the rows whose competing entities record fuzzily matches to individuals . the minimum first held record of these rows is 1931 .
Output: yes
Command: eq { min { filter_eq { all_rows ; competing entities ; individuals } ; first held } ; 1931 }, interpretation: for the result records of all rows , most of them fuzzily match to not nominated .
Output: no
Command: most_eq { all_rows ; result ; not nominated }, interpretation: select the rows whose opponent record fuzzily matches to pittsburgh steelers . select the row whose attendance record of these rows is maximum . the date record of this row is september 19 , 2004 .
Output: no
Command: eq { hop { argmax { filter_eq { all_rows ; opponent ; pittsburgh steelers } ; attendance } ; date } ; september 19 , 2004 }, interpretation: select the rows whose opponent record fuzzily matches to pittsburgh steelers . select the row whose attendance record of these rows is maximum . the date record of this row is september 19 , 2004 .
Output: yes
Command: eq { hop { argmax { filter_eq { all_rows ; opponent ; pittsburgh steelers } ; attendance } ; date } ; september 19 , 2004 }, interpretation: select the row whose pts record of all rows is maximum . the entrant record of this row is leyton house march racing team .
Output: no
Command: eq { hop { argmax { all_rows ; pts } ; entrant } ; leyton house march racing team }, interpretation: select the row whose pts record of all rows is maximum . the entrant record of this row is leyton house march racing team .
Output: yes
Command: eq { hop { argmax { all_rows ; pts } ; entrant } ; leyton house march racing team }, interpretation: select the rows whose gender record fuzzily matches to m . among these rows , select the rows whose votes record is less than 3000 . there is only one such row in the table . the candidate 's name record of this unqiue row is lorne robinson .
Output: no
Command: and { only { filter_less { filter_eq { all_rows ; gender ; m } ; votes ; 3000 } } ; eq { hop { filter_less { filter_eq { all_rows ; gender ; m } ; votes ; 3000 } ; candidate 's name } ; lorne robinson } }, interpretation: select the rows whose gender record fuzzily matches to m . among these rows , select the rows whose votes record is less than 3000 . there is only one such row in the table . the candidate 's name record of this unqiue row is lorne robinson .
Output: yes
Command: and { only { filter_less { filter_eq { all_rows ; gender ; m } ; votes ; 3000 } } ; eq { hop { filter_less { filter_eq { all_rows ; gender ; m } ; votes ; 3000 } ; candidate 's name } ; lorne robinson } }, interpretation: select the rows whose album record fuzzily matches to lost souls . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; album ; lost souls } } ; 3 }, interpretation: select the rows whose album record fuzzily matches to lost souls . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; album ; lost souls } } ; 3 }, interpretation: select the rows whose score record is equal to 64 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; score ; 64 } } ; 2 }, interpretation: select the rows whose score record is equal to 64 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; score ; 64 } } ; 2 }, interpretation: the average of the crowd record of all rows is 21012 .
Output: no
Command: round_eq { avg { all_rows ; crowd } ; 21012 }, interpretation: the average of the crowd record of all rows is 21012 .
Output: yes
Command: round_eq { avg { all_rows ; crowd } ; 21012 }, interpretation: select the row whose crowd record of all rows is 1st maximum . the venue record of this row is princes park .
Output: no
Command: eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; princes park }, interpretation: select the rows whose winner record fuzzily matches to tony longhurst . there is only one such row in the table . the series record of this unqiue row is atcc round 5 .
Output: no
Command: and { only { filter_eq { all_rows ; winner ; tony longhurst } } ; eq { hop { filter_eq { all_rows ; winner ; tony longhurst } ; series } ; atcc round 5 } }, interpretation: select the rows whose winner record fuzzily matches to tony longhurst . there is only one such row in the table . the series record of this unqiue row is atcc round 5 .
Output: yes
Command: and { only { filter_eq { all_rows ; winner ; tony longhurst } } ; eq { hop { filter_eq { all_rows ; winner ; tony longhurst } ; series } ; atcc round 5 } }, interpretation: the average of the us viewers ( millions ) record of all rows is 2.46 .
Output: no
Command: round_eq { avg { all_rows ; us viewers ( millions ) } ; 2.46 }, interpretation: the average of the us viewers ( millions ) record of all rows is 2.46 .
Output: yes
Command: round_eq { avg { all_rows ; us viewers ( millions ) } ; 2.46 }, interpretation: select the rows whose result record fuzzily matches to re - elected . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; result ; re - elected } } ; 5 }, interpretation: select the rows whose result record fuzzily matches to re - elected . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { all_rows ; result ; re - elected } } ; 5 }, interpretation: select the rows whose class record fuzzily matches to c1 . there is only one such row in the table . the year record of this unqiue row is 1992 .
Output: no
Command: and { only { filter_eq { all_rows ; class ; c1 } } ; eq { hop { filter_eq { all_rows ; class ; c1 } ; year } ; 1992 } }, interpretation: select the rows whose class record fuzzily matches to c1 . there is only one such row in the table . the year record of this unqiue row is 1992 .
Output: yes
Command: and { only { filter_eq { all_rows ; class ; c1 } } ; eq { hop { filter_eq { all_rows ; class ; c1 } ; year } ; 1992 } }, interpretation: select the rows whose games record is greater than 30 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_greater { all_rows ; games ; 30 } } ; 3 }, interpretation: select the rows whose games record is greater than 30 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_greater { all_rows ; games ; 30 } } ; 3 }, interpretation: select the row whose original air date record of all rows is minimum . the writer record of this row is michael chaplin .
Output: no
Command: eq { hop { argmin { all_rows ; original air date } ; writer } ; michael chaplin }, interpretation: select the row whose original air date record of all rows is minimum . the writer record of this row is michael chaplin .
Output: yes
Command: eq { hop { argmin { all_rows ; original air date } ; writer } ; michael chaplin }, interpretation: select the row whose laps record of all rows is minimum . the driver record of this row is mike wilds .
Output: no
Command: eq { hop { argmin { all_rows ; laps } ; driver } ; mike wilds }, interpretation: select the row whose laps record of all rows is minimum . the driver record of this row is mike wilds .
Output: yes
Command: eq { hop { argmin { all_rows ; laps } ; driver } ; mike wilds }, interpretation: select the row whose runs record of all rows is 2nd maximum . the batsmen record of this row is mark pettini jason gallian .
Output: no
Command: eq { hop { nth_argmax { all_rows ; runs ; 2 } ; batsmen } ; mark pettini jason gallian }, interpretation: select the row whose runs record of all rows is 2nd maximum . the batsmen record of this row is mark pettini jason gallian .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; runs ; 2 } ; batsmen } ; mark pettini jason gallian }, interpretation: select the rows whose class record fuzzily matches to p 2 . select the row whose quantity record of these rows is minimum . the year ( s ) built record of this row is 1886 .
Output: no
Command: eq { hop { argmin { filter_eq { all_rows ; class ; p 2 } ; quantity } ; year ( s ) built } ; 1886 }, interpretation: select the rows whose class record fuzzily matches to p 2 . select the row whose quantity record of these rows is minimum . the year ( s ) built record of this row is 1886 .
Output: yes
Command: eq { hop { argmin { filter_eq { all_rows ; class ; p 2 } ; quantity } ; year ( s ) built } ; 1886 }, interpretation: select the row whose date record of all rows is 2nd minimum . the venue record of this row is bahrain national stadium , manama .
Output: no
Command: eq { hop { nth_argmin { all_rows ; date ; 2 } ; venue } ; bahrain national stadium , manama }, interpretation: select the row whose date record of all rows is 2nd minimum . the venue record of this row is bahrain national stadium , manama .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; date ; 2 } ; venue } ; bahrain national stadium , manama }, interpretation: select the rows whose top - 10 record is not equal to 0 . there is only one such row in the table . the tournament record of this unqiue row is the open championship .
Output: no
Command: and { only { filter_not_eq { all_rows ; top - 10 ; 0 } } ; eq { hop { filter_not_eq { all_rows ; top - 10 ; 0 } ; tournament } ; the open championship } }, interpretation: select the rows whose top - 10 record is not equal to 0 . there is only one such row in the table . the tournament record of this unqiue row is the open championship .
Output: yes
Command: and { only { filter_not_eq { all_rows ; top - 10 ; 0 } } ; eq { hop { filter_not_eq { all_rows ; top - 10 ; 0 } ; tournament } ; the open championship } }, interpretation: select the row whose attendance record of all rows is maximum . the opponent record of this row is melbourne storm .
Output: no
Command: eq { hop { argmax { all_rows ; attendance } ; opponent } ; melbourne storm }, interpretation: select the row whose attendance record of all rows is maximum . the opponent record of this row is melbourne storm .
Output: yes
Command: eq { hop { argmax { all_rows ; attendance } ; opponent } ; melbourne storm }, interpretation: select the rows whose name record fuzzily matches to phil karn . take the goals record of this row . select the rows whose name record fuzzily matches to michael apple . take the goals record of this row . the first record is 2 larger than the second record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; name ; phil karn } ; goals } ; hop { filter_eq { all_rows ; name ; michael apple } ; goals } } ; 2 }, interpretation: select the rows whose name record fuzzily matches to phil karn . take the goals record of this row . select the rows whose name record fuzzily matches to michael apple . take the goals record of this row . the first record is 2 larger than the second record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; name ; phil karn } ; goals } ; hop { filter_eq { all_rows ; name ; michael apple } ; goals } } ; 2 }, interpretation: select the rows whose venue record fuzzily matches to princes park . take the crowd record of this row . select the rows whose venue record fuzzily matches to victoria park . take the crowd record of this row . the first record is 1000 larger than the second record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; venue ; princes park } ; crowd } ; hop { filter_eq { all_rows ; venue ; victoria park } ; crowd } } ; 1000 }, interpretation: select the rows whose venue record fuzzily matches to princes park . take the crowd record of this row . select the rows whose venue record fuzzily matches to victoria park . take the crowd record of this row . the first record is 1000 larger than the second record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; venue ; princes park } ; crowd } ; hop { filter_eq { all_rows ; venue ; victoria park } ; crowd } } ; 1000 }, interpretation: select the row whose founded record of all rows is minimum . the school record of this row is lockland high school .
Output: no
Command: eq { hop { argmin { all_rows ; founded } ; school } ; lockland high school }, interpretation: select the row whose founded record of all rows is minimum . the school record of this row is lockland high school .
Output: yes
Command: eq { hop { argmin { all_rows ; founded } ; school } ; lockland high school }, interpretation: select the row whose viewing figure record of all rows is maximum . the episode record of this row is episode 11 .
Output: no
Command: eq { hop { argmax { all_rows ; viewing figure } ; episode } ; episode 11 }, interpretation: select the row whose viewing figure record of all rows is maximum . the episode record of this row is episode 11 .
Output: yes
Command: eq { hop { argmax { all_rows ; viewing figure } ; episode } ; episode 11 }, interpretation: select the rows whose score record fuzzily matches to 0 . among these rows , select the rows whose attendance record is greater than 4000 . there is only one such row in the table . the home team record of this unqiue row is milton keynes dons .
Output: no
Command: and { only { filter_greater { filter_eq { all_rows ; score ; 0 } ; attendance ; 4000 } } ; eq { hop { filter_greater { filter_eq { all_rows ; score ; 0 } ; attendance ; 4000 } ; home team } ; milton keynes dons } }, interpretation: select the rows whose score record fuzzily matches to 0 . among these rows , select the rows whose attendance record is greater than 4000 . there is only one such row in the table . the home team record of this unqiue row is milton keynes dons .
Output: yes
Command: and { only { filter_greater { filter_eq { all_rows ; score ; 0 } ; attendance ; 4000 } } ; eq { hop { filter_greater { filter_eq { all_rows ; score ; 0 } ; attendance ; 4000 } ; home team } ; milton keynes dons } }, interpretation: for the year records of all rows , most of them fuzzily match to 198 .
Output: no
Command: most_eq { all_rows ; year ; 198 }, interpretation: for the year records of all rows , most of them fuzzily match to 198 .
Output: yes
Command: most_eq { all_rows ; year ; 198 }, interpretation: select the rows whose country record fuzzily matches to spain . there is only one such row in the table . the player record of this unqiue row is josé maría olazábal .
Output: no
Command: and { only { filter_eq { all_rows ; country ; spain } } ; eq { hop { filter_eq { all_rows ; country ; spain } ; player } ; josé maría olazábal } }, interpretation: select the rows whose country record fuzzily matches to spain . there is only one such row in the table . the player record of this unqiue row is josé maría olazábal .
Output: yes
Command: and { only { filter_eq { all_rows ; country ; spain } } ; eq { hop { filter_eq { all_rows ; country ; spain } ; player } ; josé maría olazábal } }, 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 -1 . there is only one such row in the table . the place record of this unqiue row is 3 .
Output: no
Command: and { only { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; -1 } } ; eq { hop { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; -1 } ; place } ; 3 } }, 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 -1 . there is only one such row in the table . the place record of this unqiue row is 3 .
Output: yes
Command: and { only { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; -1 } } ; eq { hop { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; -1 } ; place } ; 3 } }, interpretation: select the row whose management and support record of all rows is maximum . the administrating agencies by nip funds only record of this row is 0 consolidated cryptologic program .
Output: no
Command: eq { hop { argmax { all_rows ; management and support } ; administrating agencies by nip funds only } ; 0 consolidated cryptologic program }, interpretation: select the row whose management and support record of all rows is maximum . the administrating agencies by nip funds only record of this row is 0 consolidated cryptologic program .
Output: yes
Command: eq { hop { argmax { all_rows ; management and support } ; administrating agencies by nip funds only } ; 0 consolidated cryptologic program }, interpretation: select the row whose distance / duration record of all rows is 2nd minimum . the year record of this row is 2010 .
Output: no
Command: eq { hop { nth_argmin { all_rows ; distance / duration ; 2 } ; year } ; 2010 }, interpretation: select the row whose distance / duration record of all rows is 2nd minimum . the year record of this row is 2010 .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; distance / duration ; 2 } ; year } ; 2010 }, interpretation: select the row whose points record of all rows is maximum . the artist record of this row is marlen angelidou .
Output: no
Command: eq { hop { argmax { all_rows ; points } ; artist } ; marlen angelidou }, interpretation: select the row whose points record of all rows is maximum . the artist record of this row is marlen angelidou .
Output: yes
Command: eq { hop { argmax { all_rows ; points } ; artist } ; marlen angelidou }, interpretation: the average of the crowd record of all rows is 17750 .
Output: no
Command: round_eq { avg { all_rows ; crowd } ; 17750 }, interpretation: the average of the crowd record of all rows is 17750 .
Output: yes
Command: round_eq { avg { all_rows ; crowd } ; 17750 }, interpretation: the average of the to par record of all rows is -5.94 .
Output: no
Command: round_eq { avg { all_rows ; to par } ; -5.94 }, interpretation: the average of the to par record of all rows is -5.94 .
Output: yes
Command: round_eq { avg { all_rows ; to par } ; -5.94 }, interpretation: the average of the gold record of all rows is 3.29 .
Output: no
Command: round_eq { avg { all_rows ; gold } ; 3.29 }, interpretation: the average of the gold record of all rows is 3.29 .
Output: yes
Command: round_eq { avg { all_rows ; gold } ; 3.29 }, interpretation: select the rows whose td 's record is equal to 17 . there is only one such row in the table . the player name record of this unqiue row is torrance marshall .
Output: no
Command: and { only { filter_eq { all_rows ; td 's ; 17 } } ; eq { hop { filter_eq { all_rows ; td 's ; 17 } ; player name } ; torrance marshall } }, interpretation: select the rows whose td 's record is equal to 17 . there is only one such row in the table . the player name record of this unqiue row is torrance marshall .
Output: yes
Command: and { only { filter_eq { all_rows ; td 's ; 17 } } ; eq { hop { filter_eq { all_rows ; td 's ; 17 } ; player name } ; torrance marshall } }, interpretation: select the rows whose capacity record is greater than 30000 . among these rows , select the rows whose stadium record fuzzily matches to stadio giuseppe meazza . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_greater { all_rows ; capacity ; 30000 } ; stadium ; stadio giuseppe meazza } } ; 2 }, interpretation: select the rows whose capacity record is greater than 30000 . among these rows , select the rows whose stadium record fuzzily matches to stadio giuseppe meazza . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_greater { all_rows ; capacity ; 30000 } ; stadium ; stadio giuseppe meazza } } ; 2 }, interpretation: for the eliminated by records of all rows , most of them fuzzily match to carlito .
Output: no
Command: most_eq { all_rows ; eliminated by ; carlito }, interpretation: for the eliminated by records of all rows , most of them fuzzily match to carlito .
Output: yes
Command: most_eq { all_rows ; eliminated by ; carlito }, interpretation: select the rows whose school record fuzzily matches to avon community . take the enrollment record of this row . select the rows whose school record fuzzily matches to brownsburg . take the enrollment record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; school ; avon community } ; enrollment } ; hop { filter_eq { all_rows ; school ; brownsburg } ; enrollment } }, interpretation: select the rows whose school record fuzzily matches to avon community . take the enrollment record of this row . select the rows whose school record fuzzily matches to brownsburg . take the enrollment record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; school ; avon community } ; enrollment } ; hop { filter_eq { all_rows ; school ; brownsburg } ; enrollment } }, interpretation: for the bandwidth included records of all rows , all of them fuzzily match to unlimited mb .
Output: no
Command: all_eq { all_rows ; bandwidth included ; unlimited mb }, interpretation: for the bandwidth included records of all rows , all of them fuzzily match to unlimited mb .
Output: yes
Command: all_eq { all_rows ; bandwidth included ; unlimited mb }, interpretation: the sum of the result record of all rows is 456 .
Output: no
Command: round_eq { sum { all_rows ; result } ; 456 }, interpretation: the sum of the result record of all rows is 456 .
Output: yes
Command: round_eq { sum { all_rows ; result } ; 456 }, interpretation: select the rows whose peak record is equal to 1 . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; peak ; 1 } } ; 4 }, interpretation: select the rows whose peak record is equal to 1 . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; peak ; 1 } } ; 4 }, interpretation: select the rows whose player record fuzzily matches to pascal chimbonda . take the total record of this row . select the rows whose player record fuzzily matches to robbie keane . take the total record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; player ; pascal chimbonda } ; total } ; hop { filter_eq { all_rows ; player ; robbie keane } ; total } }, interpretation: select the rows whose player record fuzzily matches to pascal chimbonda . take the total record of this row . select the rows whose player record fuzzily matches to robbie keane . take the total record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; player ; pascal chimbonda } ; total } ; hop { filter_eq { all_rows ; player ; robbie keane } ; total } }, interpretation: select the row whose time record of all rows is minimum . the name record of this row is pieter van den hoogenband .
Output: no
Command: eq { hop { argmin { all_rows ; time } ; name } ; pieter van den hoogenband }, interpretation: select the row whose time record of all rows is minimum . the name record of this row is pieter van den hoogenband .
Output: yes
Command: eq { hop { argmin { all_rows ; time } ; name } ; pieter van den hoogenband }, interpretation: select the rows whose incumbent record fuzzily matches to john page . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to francis walker . take the first elected record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; incumbent ; john page } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; francis walker } ; first elected } }, interpretation: select the rows whose incumbent record fuzzily matches to john page . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to francis walker . take the first elected record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; incumbent ; john page } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; francis walker } ; first elected } }, interpretation: the average of the 1990 - 95 record of all rows is 0.27 .
Output: no
Command: round_eq { avg { all_rows ; 1990 - 95 } ; 0.27 }, interpretation: the average of the 1990 - 95 record of all rows is 0.27 .
Output: yes
Command: round_eq { avg { all_rows ; 1990 - 95 } ; 0.27 }, interpretation: select the rows whose location record fuzzily matches to bridgewater , massachusetts . take the enrollment record of this row . select the rows whose location record fuzzily matches to fitchburg , massachusetts . take the enrollment record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; location ; bridgewater , massachusetts } ; enrollment } ; hop { filter_eq { all_rows ; location ; fitchburg , massachusetts } ; enrollment } }, interpretation: select the rows whose location record fuzzily matches to bridgewater , massachusetts . take the enrollment record of this row . select the rows whose location record fuzzily matches to fitchburg , massachusetts . take the enrollment record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; location ; bridgewater , massachusetts } ; enrollment } ; hop { filter_eq { all_rows ; location ; fitchburg , massachusetts } ; enrollment } }, interpretation: select the rows whose first elected record is equal to 2002 . there is only one such row in the table . the incumbent record of this unqiue row is chris chocola .
Output: no
Command: and { only { filter_eq { all_rows ; first elected ; 2002 } } ; eq { hop { filter_eq { all_rows ; first elected ; 2002 } ; incumbent } ; chris chocola } }, interpretation: select the rows whose first elected record is equal to 2002 . there is only one such row in the table . the incumbent record of this unqiue row is chris chocola .
Output: yes
Command: and { only { filter_eq { all_rows ; first elected ; 2002 } } ; eq { hop { filter_eq { all_rows ; first elected ; 2002 } ; incumbent } ; chris chocola } }, interpretation: select the row whose points record of all rows is maximum . the final placing record of this row is 11th .
Output: no
Command: eq { hop { argmax { all_rows ; points } ; final placing } ; 11th }, interpretation: select the row whose points record of all rows is maximum . the final placing record of this row is 11th .
Output: yes
Command: eq { hop { argmax { all_rows ; points } ; final placing } ; 11th }, interpretation: select the rows whose ends won record is greater than 50 . among these rows , select the rows whose ends lost record is less than 50 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_less { filter_greater { all_rows ; ends won ; 50 } ; ends lost ; 50 } } ; 2 }, interpretation: select the rows whose ends won record is greater than 50 . among these rows , select the rows whose ends lost record is less than 50 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_less { filter_greater { all_rows ; ends won ; 50 } ; ends lost ; 50 } } ; 2 }, interpretation: select the rows whose result record fuzzily matches to retired . there is only one such row in the table . the incumbent record of this unqiue row is edward boland .
Output: no
Command: and { only { filter_eq { all_rows ; result ; retired } } ; eq { hop { filter_eq { all_rows ; result ; retired } ; incumbent } ; edward boland } }, interpretation: select the rows whose result record fuzzily matches to retired . there is only one such row in the table . the incumbent record of this unqiue row is edward boland .
Output: yes
Command: and { only { filter_eq { all_rows ; result ; retired } } ; eq { hop { filter_eq { all_rows ; result ; retired } ; incumbent } ; edward boland } }, interpretation: select the rows whose location record fuzzily matches to ga . among these rows , select the rows whose champion record fuzzily matches to emory . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; location ; ga } ; champion ; emory } } ; 2 }, interpretation: select the rows whose location record fuzzily matches to ga . among these rows , select the rows whose champion record fuzzily matches to emory . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; location ; ga } ; champion ; emory } } ; 2 }, interpretation: select the row whose events record of all rows is maximum . the player record of this row is jim thorpe .
Output: no
Command: eq { hop { argmax { all_rows ; events } ; player } ; jim thorpe }, interpretation: select the row whose events record of all rows is maximum . the player record of this row is jim thorpe .
Output: yes
Command: eq { hop { argmax { all_rows ; events } ; player } ; jim thorpe }, interpretation: for the venue records of all rows , most of them fuzzily match to amman .
Output: no
Command: most_eq { all_rows ; venue ; amman }, interpretation: for the venue records of all rows , most of them fuzzily match to amman .
Output: yes
Command: most_eq { all_rows ; venue ; amman }, interpretation: for the agg records of all rows , most of them are not equal to 0 .
Output: no
Command: most_not_eq { all_rows ; agg ; 0 }, interpretation: for the agg records of all rows , most of them are not equal to 0 .
Output: yes
Command: most_not_eq { all_rows ; agg ; 0 }, interpretation: select the row whose production ( mt ) record of all rows is maximum . the commodity record of this row is wheat .
Output: no
Command: eq { hop { argmax { all_rows ; production ( mt ) } ; commodity } ; wheat }, interpretation: select the row whose production ( mt ) record of all rows is maximum . the commodity record of this row is wheat .
Output: yes
Command: eq { hop { argmax { all_rows ; production ( mt ) } ; commodity } ; wheat }, interpretation: the 3rd maximum winnings record of all rows is 160261 .
Output: no
Command: eq { nth_max { all_rows ; winnings ; 3 } ; 160261 }, interpretation: the 3rd maximum winnings record of all rows is 160261 .
Output: yes