niryuu/sni-each-converted huggingface dataset:
Command: most_not_eq { all_rows ; event ; team competitions }, interpretation: select the rows whose points record is less than 20 . among these rows , select the rows whose draw record is equal to 2 . there is only one such row in the table . the team record of this unqiue row is wanda nowa huta .
Output: no
Command: and { only { filter_eq { filter_less { all_rows ; points ; 20 } ; draw ; 2 } } ; eq { hop { filter_eq { filter_less { all_rows ; points ; 20 } ; draw ; 2 } ; team } ; wanda nowa huta } }, interpretation: select the rows whose points record is less than 20 . among these rows , select the rows whose draw record is equal to 2 . there is only one such row in the table . the team record of this unqiue row is wanda nowa huta .
Output: yes
Command: and { only { filter_eq { filter_less { all_rows ; points ; 20 } ; draw ; 2 } } ; eq { hop { filter_eq { filter_less { all_rows ; points ; 20 } ; draw ; 2 } ; team } ; wanda nowa huta } }, interpretation: select the row whose attendance record of all rows is 2nd maximum . the opponent record of this row is denver broncos .
Output: no
Command: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; opponent } ; denver broncos }, interpretation: select the row whose attendance record of all rows is 2nd maximum . the opponent record of this row is denver broncos .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; opponent } ; denver broncos }, interpretation: the average of the attendance record of all rows is 16119 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 16119 }, interpretation: the average of the attendance record of all rows is 16119 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 16119 }, interpretation: select the rows whose playoffs record fuzzily matches to did not qualify . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; playoffs ; did not qualify } } ; 2 }, interpretation: select the rows whose playoffs record fuzzily matches to did not qualify . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; playoffs ; did not qualify } } ; 2 }, interpretation: the average of the attendance record of all rows is 43800 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 43800 }, interpretation: the average of the attendance record of all rows is 43800 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 43800 }, interpretation: select the rows whose writer record fuzzily matches to ed roe . there is only one such row in the table . the production code record of this unqiue row is 402 .
Output: no
Command: and { only { filter_eq { all_rows ; writer ; ed roe } } ; eq { hop { filter_eq { all_rows ; writer ; ed roe } ; production code } ; 402 } }, interpretation: select the rows whose writer record fuzzily matches to ed roe . there is only one such row in the table . the production code record of this unqiue row is 402 .
Output: yes
Command: and { only { filter_eq { all_rows ; writer ; ed roe } } ; eq { hop { filter_eq { all_rows ; writer ; ed roe } ; production code } ; 402 } }, interpretation: select the row whose administrative panel record of all rows is maximum . the party record of this row is fine gael .
Output: no
Command: eq { hop { argmax { all_rows ; administrative panel } ; party } ; fine gael }, interpretation: select the row whose administrative panel record of all rows is maximum . the party record of this row is fine gael .
Output: yes
Command: eq { hop { argmax { all_rows ; administrative panel } ; party } ; fine gael }, interpretation: select the rows whose tournaments played record is greater than 20 . there is only one such row in the table .
Output: no
Command: only { filter_greater { all_rows ; tournaments played ; 20 } }, interpretation: select the rows whose tournaments played record is greater than 20 . there is only one such row in the table .
Output: yes
Command: only { filter_greater { all_rows ; tournaments played ; 20 } }, interpretation: for the score records of all rows , most of them are greater than or equal to 5 .
Output: no
Command: most_greater_eq { all_rows ; score ; 5 }, interpretation: for the score records of all rows , most of them are greater than or equal to 5 .
Output: yes
Command: most_greater_eq { all_rows ; score ; 5 }, interpretation: select the rows whose date record fuzzily matches to october . among these rows , select the rows whose result record fuzzily matches to w . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; date ; october } ; result ; w } } ; 3 }, interpretation: select the rows whose date record fuzzily matches to october . among these rows , select the rows whose result record fuzzily matches to w . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; date ; october } ; result ; w } } ; 3 }, interpretation: for the party records of all rows , most of them fuzzily match to republican .
Output: no
Command: most_eq { all_rows ; party ; republican }, interpretation: select the rows whose score record is equal to 0 . the number of such rows is 7 .
Output: no
Command: eq { count { filter_eq { all_rows ; score ; 0 } } ; 7 }, interpretation: select the rows whose score record is equal to 0 . the number of such rows is 7 .
Output: yes
Command: eq { count { filter_eq { all_rows ; score ; 0 } } ; 7 }, interpretation: select the rows whose 2013 date record fuzzily matches to may . among these rows , select the rows whose local name record fuzzily matches to празник рада , praznik rada . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; 2013 date ; may } ; local name ; празник рада , praznik rada } } ; 2 }, interpretation: select the rows whose 2013 date record fuzzily matches to may . among these rows , select the rows whose local name record fuzzily matches to празник рада , praznik rada . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; 2013 date ; may } ; local name ; празник рада , praznik rada } } ; 2 }, interpretation: the average of the weight record of all rows is 88 .
Output: no
Command: round_eq { avg { all_rows ; weight } ; 88 }, interpretation: the average of the weight record of all rows is 88 .
Output: yes
Command: round_eq { avg { all_rows ; weight } ; 88 }, interpretation: select the row whose notes record of all rows is 3rd maximum . the year record of this row is 2004 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; notes ; 3 } ; year } ; 2004 }, interpretation: select the row whose notes record of all rows is 3rd maximum . the year record of this row is 2004 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; notes ; 3 } ; year } ; 2004 }, interpretation: for the attendance records of all rows , most of them are greater than or equal to 60000 .
Output: no
Command: most_greater_eq { all_rows ; attendance ; 60000 }, interpretation: for the attendance records of all rows , most of them are greater than or equal to 60000 .
Output: yes
Command: most_greater_eq { all_rows ; attendance ; 60000 }, interpretation: for the result records of all rows , most of them fuzzily match to won .
Output: no
Command: most_eq { all_rows ; result ; won }, interpretation: select the rows whose nationality record fuzzily matches to australia . there is only one such row in the table . the name record of this unqiue row is michael klim .
Output: no
Command: and { only { filter_eq { all_rows ; nationality ; australia } } ; eq { hop { filter_eq { all_rows ; nationality ; australia } ; name } ; michael klim } }, interpretation: select the rows whose nationality record fuzzily matches to australia . there is only one such row in the table . the name record of this unqiue row is michael klim .
Output: yes
Command: and { only { filter_eq { all_rows ; nationality ; australia } } ; eq { hop { filter_eq { all_rows ; nationality ; australia } ; name } ; michael klim } }, interpretation: select the row whose points record of all rows is maximum . the points record of this row is 227 .
Output: no
Command: eq { hop { argmax { all_rows ; points } ; points } ; 227 }, interpretation: select the row whose points record of all rows is maximum . the points record of this row is 227 .
Output: yes
Command: eq { hop { argmax { all_rows ; points } ; points } ; 227 }, interpretation: select the rows whose space agency record fuzzily matches to nasa . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; space agency ; nasa } } ; 3 }, interpretation: select the rows whose space agency record fuzzily matches to nasa . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; space agency ; nasa } } ; 3 }, interpretation: the sum of the pts record of all rows is 23 .
Output: no
Command: round_eq { sum { all_rows ; pts } ; 23 }, interpretation: the sum of the pts record of all rows is 23 .
Output: yes
Command: round_eq { sum { all_rows ; pts } ; 23 }, interpretation: select the rows whose manufacturer record fuzzily matches to ford . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; manufacturer ; ford } } ; 3 }, interpretation: select the rows whose manufacturer record fuzzily matches to ford . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; manufacturer ; ford } } ; 3 }, interpretation: select the rows whose home team record fuzzily matches to fitzroy . take the home team score record of this row . select the rows whose home team record fuzzily matches to collingwood . 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 ; fitzroy } ; home team score } ; hop { filter_eq { all_rows ; home team ; collingwood } ; home team score } }, interpretation: select the rows whose home team record fuzzily matches to fitzroy . take the home team score record of this row . select the rows whose home team record fuzzily matches to collingwood . take the home team score record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; home team ; fitzroy } ; home team score } ; hop { filter_eq { all_rows ; home team ; collingwood } ; home team score } }, interpretation: select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is tom barrett .
Output: no
Command: eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; tom barrett }, interpretation: select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is tom barrett .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; tom barrett }, interpretation: select the rows whose max height ( ft ) record is greater than or equal to 20000 . among these rows , select the rows whose m / v ft / s record is greater than or equal to 2200 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_greater_eq { filter_greater_eq { all_rows ; max height ( ft ) ; 20000 } ; m / v ft / s ; 2200 } } ; 3 }, interpretation: select the rows whose max height ( ft ) record is greater than or equal to 20000 . among these rows , select the rows whose m / v ft / s record is greater than or equal to 2200 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_greater_eq { filter_greater_eq { all_rows ; max height ( ft ) ; 20000 } ; m / v ft / s ; 2200 } } ; 3 }, interpretation: select the rows whose year record fuzzily matches to 2000 . take the position record of this row . select the rows whose year record fuzzily matches to 2004 . take the position record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; year ; 2000 } ; position } ; hop { filter_eq { all_rows ; year ; 2004 } ; position } }, interpretation: select the rows whose year record fuzzily matches to 2000 . take the position record of this row . select the rows whose year record fuzzily matches to 2004 . take the position record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; year ; 2000 } ; position } ; hop { filter_eq { all_rows ; year ; 2004 } ; position } }, interpretation: select the rows whose years record is equal to 2010 . the average of the power record of these rows is 3875 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; years ; 2010 } ; power } ; 3875 }, interpretation: select the rows whose years record is equal to 2010 . the average of the power record of these rows is 3875 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; years ; 2010 } ; power } ; 3875 }, interpretation: select the rows whose events record is equal to 0 . there is only one such row in the table . the tournament record of this unqiue row is us open .
Output: no
Command: and { only { filter_eq { all_rows ; events ; 0 } } ; eq { hop { filter_eq { all_rows ; events ; 0 } ; tournament } ; us open } }, interpretation: select the rows whose events record is equal to 0 . there is only one such row in the table . the tournament record of this unqiue row is us open .
Output: yes
Command: and { only { filter_eq { all_rows ; events ; 0 } } ; eq { hop { filter_eq { all_rows ; events ; 0 } ; tournament } ; us open } }, interpretation: select the rows whose player record fuzzily matches to kj choi . take the reset points record of this row . select the rows whose player record fuzzily matches to charles howell iii . take the reset points record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; player ; kj choi } ; reset points } ; hop { filter_eq { all_rows ; player ; charles howell iii } ; reset points } }, interpretation: select the rows whose player record fuzzily matches to kj choi . take the reset points record of this row . select the rows whose player record fuzzily matches to charles howell iii . take the reset points record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; player ; kj choi } ; reset points } ; hop { filter_eq { all_rows ; player ; charles howell iii } ; reset points } }, interpretation: select the rows whose written by record fuzzily matches to david wolkove . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; written by ; david wolkove } } ; 2 }, interpretation: select the rows whose written by record fuzzily matches to david wolkove . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; written by ; david wolkove } } ; 2 }, interpretation: select the rows whose type record fuzzily matches to private . the number of such rows is 12 .
Output: no
Command: eq { count { filter_eq { all_rows ; type ; private } } ; 12 }, interpretation: select the rows whose type record fuzzily matches to private . the number of such rows is 12 .
Output: yes
Command: eq { count { filter_eq { all_rows ; type ; private } } ; 12 }, interpretation: select the rows whose colour record fuzzily matches to brown . there is only one such row in the table . the name record of this unqiue row is ted .
Output: no
Command: and { only { filter_eq { all_rows ; colour ; brown } } ; eq { hop { filter_eq { all_rows ; colour ; brown } ; name } ; ted } }, interpretation: select the rows whose colour record fuzzily matches to brown . there is only one such row in the table . the name record of this unqiue row is ted .
Output: yes
Command: and { only { filter_eq { all_rows ; colour ; brown } } ; eq { hop { filter_eq { all_rows ; colour ; brown } ; name } ; ted } }, interpretation: the sum of the parishes record of all rows is 3149 .
Output: no
Command: round_eq { sum { all_rows ; parishes } ; 3149 }, interpretation: the sum of the parishes record of all rows is 3149 .
Output: yes
Command: round_eq { sum { all_rows ; parishes } ; 3149 }, interpretation: select the rows whose hpfs record fuzzily matches to partial . there is only one such row in the table . the ntfs record of this unqiue row is yes v1 .2 .
Output: no
Command: and { only { filter_eq { all_rows ; hpfs ; partial } } ; eq { hop { filter_eq { all_rows ; hpfs ; partial } ; ntfs } ; yes v1 .2 } }, interpretation: select the rows whose hpfs record fuzzily matches to partial . there is only one such row in the table . the ntfs record of this unqiue row is yes v1 .2 .
Output: yes
Command: and { only { filter_eq { all_rows ; hpfs ; partial } } ; eq { hop { filter_eq { all_rows ; hpfs ; partial } ; ntfs } ; yes v1 .2 } }, interpretation: select the row whose date record of all rows is 3rd minimum . the centerfold model record of this row is sandra hubby .
Output: no
Command: eq { hop { nth_argmin { all_rows ; date ; 3 } ; centerfold model } ; sandra hubby }, interpretation: select the row whose date record of all rows is 3rd minimum . the centerfold model record of this row is sandra hubby .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; date ; 3 } ; centerfold model } ; sandra hubby }, interpretation: select the row whose home team score record of all rows is maximum . the home team record of this row is sydney .
Output: no
Command: eq { hop { argmax { all_rows ; home team score } ; home team } ; sydney }, interpretation: select the row whose home team score record of all rows is maximum . the home team record of this row is sydney .
Output: yes
Command: eq { hop { argmax { all_rows ; home team score } ; home team } ; sydney }, interpretation: select the row whose crowd record of all rows is maximum . the venue record of this row is princes park .
Output: no
Command: eq { hop { argmax { all_rows ; crowd } ; venue } ; princes park }, interpretation: select the row whose crowd record of all rows is maximum . the venue record of this row is princes park .
Output: yes
Command: eq { hop { argmax { all_rows ; crowd } ; venue } ; princes park }, interpretation: select the rows whose country record fuzzily matches to united states . the average of the to par record of these rows is 9.67 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; country ; united states } ; to par } ; 9.67 }, interpretation: select the rows whose country record fuzzily matches to united states . the average of the to par record of these rows is 9.67 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; country ; united states } ; to par } ; 9.67 }, interpretation: select the rows whose investigation record fuzzily matches to somatostatin receptor imaging . for the route of administration records of these rows , all of them fuzzily match to iv .
Output: no
Command: all_eq { filter_eq { all_rows ; investigation ; somatostatin receptor imaging } ; route of administration ; iv }, interpretation: select the rows whose investigation record fuzzily matches to somatostatin receptor imaging . for the route of administration records of these rows , all of them fuzzily match to iv .
Output: yes
Command: all_eq { filter_eq { all_rows ; investigation ; somatostatin receptor imaging } ; route of administration ; iv }, interpretation: select the row whose us viewers ( in millions ) record of all rows is maximum . the title record of this row is crazy for you .
Output: no
Command: eq { hop { argmax { all_rows ; us viewers ( in millions ) } ; title } ; crazy for you }, interpretation: select the row whose us viewers ( in millions ) record of all rows is maximum . the title record of this row is crazy for you .
Output: yes
Command: eq { hop { argmax { all_rows ; us viewers ( in millions ) } ; title } ; crazy for you }, interpretation: select the rows whose location ( city , state ) record fuzzily matches to michigan . there is only one such row in the table . the yard name record of this unqiue row is great lakes engineering co .
Output: no
Command: and { only { filter_eq { all_rows ; location ( city , state ) ; michigan } } ; eq { hop { filter_eq { all_rows ; location ( city , state ) ; michigan } ; yard name } ; great lakes engineering co } }, interpretation: select the rows whose location ( city , state ) record fuzzily matches to michigan . there is only one such row in the table . the yard name record of this unqiue row is great lakes engineering co .
Output: yes
Command: and { only { filter_eq { all_rows ; location ( city , state ) ; michigan } } ; eq { hop { filter_eq { all_rows ; location ( city , state ) ; michigan } ; yard name } ; great lakes engineering co } }, interpretation: select the rows whose competition record is arbitrary . the number of such rows is 10 .
Output: no
Command: eq { count { filter_all { all_rows ; competition } } ; 10 }, interpretation: select the rows whose competition record is arbitrary . the number of such rows is 10 .
Output: yes
Command: eq { count { filter_all { all_rows ; competition } } ; 10 }, interpretation: select the rows whose pos record fuzzily matches to dnf . there is only one such row in the table . the year record of this unqiue row is 2004 .
Output: no
Command: and { only { filter_eq { all_rows ; pos ; dnf } } ; eq { hop { filter_eq { all_rows ; pos ; dnf } ; year } ; 2004 } }, interpretation: select the rows whose pos record fuzzily matches to dnf . there is only one such row in the table . the year record of this unqiue row is 2004 .
Output: yes
Command: and { only { filter_eq { all_rows ; pos ; dnf } } ; eq { hop { filter_eq { all_rows ; pos ; dnf } ; year } ; 2004 } }, interpretation: for the songwriter ( s ) records of all rows , all of them fuzzily match to ( adapted by malcolm dodds ) .
Output: no
Command: all_eq { all_rows ; songwriter ( s ) ; ( adapted by malcolm dodds ) }, interpretation: for the songwriter ( s ) records of all rows , all of them fuzzily match to ( adapted by malcolm dodds ) .
Output: yes
Command: all_eq { all_rows ; songwriter ( s ) ; ( adapted by malcolm dodds ) }, interpretation: select the row whose time record of all rows is 1st minimum . the winner record of this row is cashier 's dream .
Output: no
Command: eq { hop { nth_argmin { all_rows ; time ; 1 } ; winner } ; cashier 's dream }, interpretation: select the row whose time record of all rows is 1st minimum . the winner record of this row is cashier 's dream .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; time ; 1 } ; winner } ; cashier 's dream }, interpretation: select the row whose cuts made record of all rows is maximum . the year record of this row is 2009 .
Output: no
Command: eq { hop { argmax { all_rows ; cuts made } ; year } ; 2009 }, interpretation: select the row whose cuts made record of all rows is maximum . the year record of this row is 2009 .
Output: yes
Command: eq { hop { argmax { all_rows ; cuts made } ; year } ; 2009 }, interpretation: select the row whose speed ( km / h ) record of all rows is 2nd maximum . the vehicle record of this row is lockheed sr - 71 blackbird .
Output: no
Command: eq { hop { nth_argmax { all_rows ; speed ( km / h ) ; 2 } ; vehicle } ; lockheed sr - 71 blackbird }, interpretation: select the row whose speed ( km / h ) record of all rows is 2nd maximum . the vehicle record of this row is lockheed sr - 71 blackbird .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; speed ( km / h ) ; 2 } ; vehicle } ; lockheed sr - 71 blackbird }, interpretation: for the year records of all rows , all of them are greater than 1970 .
Output: no
Command: all_greater { all_rows ; year ; 1970 }, interpretation: for the year records of all rows , all of them are greater than 1970 .
Output: yes
Command: all_greater { all_rows ; year ; 1970 }, 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 row whose operating expenditures record of all rows is 1st maximum . the candidate record of this row is barack obama .
Output: no
Command: eq { hop { nth_argmax { all_rows ; operating expenditures ; 1 } ; candidate } ; barack obama }, interpretation: select the row whose operating expenditures record of all rows is 1st maximum . the candidate record of this row is barack obama .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; operating expenditures ; 1 } ; candidate } ; barack obama }, interpretation: select the rows whose result record fuzzily matches to 35-34 . there is only one such row in the table . the date record of this unqiue row is november 15 .
Output: no
Command: and { only { filter_eq { all_rows ; result ; 35-34 } } ; eq { hop { filter_eq { all_rows ; result ; 35-34 } ; date } ; november 15 } }, interpretation: select the rows whose result record fuzzily matches to 35-34 . there is only one such row in the table . the date record of this unqiue row is november 15 .
Output: yes
Command: and { only { filter_eq { all_rows ; result ; 35-34 } } ; eq { hop { filter_eq { all_rows ; result ; 35-34 } ; date } ; november 15 } }, interpretation: select the rows whose launch date record fuzzily matches to june . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; launch date ; june } } ; 3 }, interpretation: select the rows whose launch date record fuzzily matches to june . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; launch date ; june } } ; 3 }, interpretation: select the rows whose result record fuzzily matches to safe . the sum of the score record of these rows is 54 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; result ; safe } ; score } ; 54 }, interpretation: select the rows whose result record fuzzily matches to safe . the sum of the score record of these rows is 54 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; result ; safe } ; score } ; 54 }, interpretation: select the rows whose namesake record fuzzily matches to sumerian town . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; namesake ; sumerian town } } ; 2 }, interpretation: select the rows whose namesake record fuzzily matches to sumerian town . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; namesake ; sumerian town } } ; 2 }, interpretation: for the high points records of all rows , most of them fuzzily match to kevin martin .
Output: no
Command: most_eq { all_rows ; high points ; kevin martin }, interpretation: for the high points records of all rows , most of them fuzzily match to kevin martin .
Output: yes
Command: most_eq { all_rows ; high points ; kevin martin }, interpretation: the sum of the attendance record of all rows is 129457 .
Output: no
Command: round_eq { sum { all_rows ; attendance } ; 129457 }, interpretation: the sum of the attendance record of all rows is 129457 .
Output: yes
Command: round_eq { sum { all_rows ; attendance } ; 129457 }, interpretation: select the rows whose venue record fuzzily matches to waverley park . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; venue ; waverley park } } ; 4 }, interpretation: select the rows whose venue record fuzzily matches to waverley park . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; venue ; waverley park } } ; 4 }, interpretation: select the rows whose points record is greater than 41 . there is only one such row in the table . the artist record of this unqiue row is extra nena .
Output: no
Command: and { only { filter_greater { all_rows ; points ; 41 } } ; eq { hop { filter_greater { all_rows ; points ; 41 } ; artist } ; extra nena } }, interpretation: select the rows whose points record is greater than 41 . there is only one such row in the table . the artist record of this unqiue row is extra nena .
Output: yes
Command: and { only { filter_greater { all_rows ; points ; 41 } } ; eq { hop { filter_greater { all_rows ; points ; 41 } ; artist } ; extra nena } }, interpretation: select the rows whose competition record fuzzily matches to 2000 summer olympics . take the class record of this row . select the rows whose competition record fuzzily matches to 1996 summer olympics . take the class record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; competition ; 2000 summer olympics } ; class } ; hop { filter_eq { all_rows ; competition ; 1996 summer olympics } ; class } }, interpretation: select the rows whose competition record fuzzily matches to 2000 summer olympics . take the class record of this row . select the rows whose competition record fuzzily matches to 1996 summer olympics . take the class record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; competition ; 2000 summer olympics } ; class } ; hop { filter_eq { all_rows ; competition ; 1996 summer olympics } ; class } }, interpretation: select the rows whose model number record fuzzily matches to 800 . the average of the frequency record of these rows is 800 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; model number ; 800 } ; frequency } ; 800 }, interpretation: select the rows whose model number record fuzzily matches to 800 . the average of the frequency record of these rows is 800 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; model number ; 800 } ; frequency } ; 800 }, interpretation: select the rows whose name record fuzzily matches to gary neville . take the total record of this row . select the rows whose name record fuzzily matches to alex stepney . 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 ; name ; gary neville } ; total } ; hop { filter_eq { all_rows ; name ; alex stepney } ; total } }, interpretation: select the rows whose name record fuzzily matches to gary neville . take the total record of this row . select the rows whose name record fuzzily matches to alex stepney . 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 ; name ; gary neville } ; total } ; hop { filter_eq { all_rows ; name ; alex stepney } ; total } }, interpretation: for the crowd records of all rows , most of them are greater than 10000 .
Output: no
Command: most_greater { all_rows ; crowd ; 10000 }, interpretation: select the rows whose first elected record fuzzily matches to 195 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; first elected ; 195 } } ; 2 }, interpretation: select the rows whose first elected record fuzzily matches to 195 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; first elected ; 195 } } ; 2 }, interpretation: select the row whose react record of all rows is minimum . the athlete record of this row is tyler christopher .
Output: no
Command: eq { hop { argmin { all_rows ; react } ; athlete } ; tyler christopher }, interpretation: select the row whose react record of all rows is minimum . the athlete record of this row is tyler christopher .
Output: yes
Command: eq { hop { argmin { all_rows ; react } ; athlete } ; tyler christopher }, interpretation: select the row whose laps record of all rows is maximum . the year record of this row is 1989 .
Output: no
Command: eq { hop { argmax { all_rows ; laps } ; year } ; 1989 }, interpretation: select the row whose laps record of all rows is maximum . the year record of this row is 1989 .
Output: yes
Command: eq { hop { argmax { all_rows ; laps } ; year } ; 1989 }, interpretation: select the rows whose crowd record is less than 10000 . the average of the crowd record of these rows is 7005 .
Output: no
Command: round_eq { avg { filter_less { all_rows ; crowd ; 10000 } ; crowd } ; 7005 }, interpretation: select the rows whose crowd record is less than 10000 . the average of the crowd record of these rows is 7005 .
Output: yes
Command: round_eq { avg { filter_less { all_rows ; crowd ; 10000 } ; crowd } ; 7005 }, interpretation: select the rows whose date record fuzzily matches to sept 15 . take the attendance record of this row . select the rows whose date record fuzzily matches to nov 3 . take the attendance record of this row . the first record is greater than the second record . the opponent record of the first row is green bay packers . the opponent record of the second row is chicago bears .
Output: no
Command: and { greater { hop { filter_eq { all_rows ; date ; sept 15 } ; attendance } ; hop { filter_eq { all_rows ; date ; nov 3 } ; attendance } } ; and { eq { hop { filter_eq { all_rows ; date ; sept 15 } ; opponent } ; green bay packers } ; eq { hop { filter_eq { all_rows ; date ; nov 3 } ; opponent } ; chicago bears } } }, interpretation: select the rows whose date record fuzzily matches to sept 15 . take the attendance record of this row . select the rows whose date record fuzzily matches to nov 3 . take the attendance record of this row . the first record is greater than the second record . the opponent record of the first row is green bay packers . the opponent record of the second row is chicago bears .
Output: yes
Command: and { greater { hop { filter_eq { all_rows ; date ; sept 15 } ; attendance } ; hop { filter_eq { all_rows ; date ; nov 3 } ; attendance } } ; and { eq { hop { filter_eq { all_rows ; date ; sept 15 } ; opponent } ; green bay packers } ; eq { hop { filter_eq { all_rows ; date ; nov 3 } ; opponent } ; chicago bears } } }, interpretation: for the director records of all rows , most of them fuzzily match to paul gay .
Output: no
Command: most_eq { all_rows ; director ; paul gay }, interpretation: for the director records of all rows , most of them fuzzily match to paul gay .
Output: yes
Command: most_eq { all_rows ; director ; paul gay }, interpretation: the average of the away team score record of all rows is 8.75 .
Output: no
Command: round_eq { avg { all_rows ; away team score } ; 8.75 }, interpretation: the average of the away team score record of all rows is 8.75 .
Output: yes
Command: round_eq { avg { all_rows ; away team score } ; 8.75 }, interpretation: select the row whose date record of all rows is 1st minimum . the winning driver record of this row is eugenio silvani .
Output: no
Command: eq { hop { nth_argmin { all_rows ; date ; 1 } ; winning driver } ; eugenio silvani }, interpretation: select the row whose date record of all rows is 1st minimum . the winning driver record of this row is eugenio silvani .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; date ; 1 } ; winning driver } ; eugenio silvani }, interpretation: select the row whose points record of all rows is maximum . the year record of this row is 1989 .
Output: no
Command: eq { hop { argmax { all_rows ; points } ; year } ; 1989 }, interpretation: select the row whose points record of all rows is maximum . the year record of this row is 1989 .
Output: yes
Command: eq { hop { argmax { all_rows ; points } ; year } ; 1989 }, interpretation: select the rows whose country of origin record fuzzily matches to colombia . there is only one such row in the table . the name / designation record of this unqiue row is saturn machine pistol .
Output: no
Command: and { only { filter_eq { all_rows ; country of origin ; colombia } } ; eq { hop { filter_eq { all_rows ; country of origin ; colombia } ; name / designation } ; saturn machine pistol } }, interpretation: select the rows whose country of origin record fuzzily matches to colombia . there is only one such row in the table . the name / designation record of this unqiue row is saturn machine pistol .
Output: yes
Command: and { only { filter_eq { all_rows ; country of origin ; colombia } } ; eq { hop { filter_eq { all_rows ; country of origin ; colombia } ; name / designation } ; saturn machine pistol } }, interpretation: the average of the elevation ( m ) record of all rows is 4390 .
Output: no
Command: round_eq { avg { all_rows ; elevation ( m ) } ; 4390 }, interpretation: the average of the elevation ( m ) record of all rows is 4390 .
Output: yes
Command: round_eq { avg { all_rows ; elevation ( m ) } ; 4390 }, interpretation: select the rows whose constructor record fuzzily matches to toyota . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; constructor ; toyota } } ; 2 }, interpretation: select the rows whose constructor record fuzzily matches to toyota . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; constructor ; toyota } } ; 2 }, interpretation: select the rows whose country record fuzzily matches to united states . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; country ; united states } } ; 3 }, interpretation: select the rows whose country record fuzzily matches to united states . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; country ; united states } } ; 3 }, interpretation: select the row whose release - year of first charted record record of all rows is minimum . the artist record of this row is frank sinatra .
Output: no
Command: eq { hop { argmin { all_rows ; release - year of first charted record } ; artist } ; frank sinatra }, interpretation: select the row whose release - year of first charted record record of all rows is minimum . the artist record of this row is frank sinatra .
Output: yes
Command: eq { hop { argmin { all_rows ; release - year of first charted record } ; artist } ; frank sinatra }, interpretation: select the row whose score record of all rows is maximum . the team 1 record of this row is as monaco ( d1 ) .
Output: no
Command: eq { hop { argmax { all_rows ; score } ; team 1 } ; as monaco ( d1 ) }, interpretation: select the row whose score record of all rows is maximum . the team 1 record of this row is as monaco ( d1 ) .
Output: yes
Command: eq { hop { argmax { all_rows ; score } ; team 1 } ; as monaco ( d1 ) }, interpretation: select the rows whose webcast link record fuzzily matches to n / a . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; webcast link ; n / a } } ; 2 }, interpretation: select the rows whose webcast link record fuzzily matches to n / a . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; webcast link ; n / a } } ; 2 }, interpretation: select the rows whose status record fuzzily matches to rural community . there is only one such row in the table . the official name record of this unqiue row is beaubassin east .
Output: no
Command: and { only { filter_eq { all_rows ; status ; rural community } } ; eq { hop { filter_eq { all_rows ; status ; rural community } ; official name } ; beaubassin east } }, interpretation: select the rows whose status record fuzzily matches to rural community . there is only one such row in the table . the official name record of this unqiue row is beaubassin east .
Output: yes
Command: and { only { filter_eq { all_rows ; status ; rural community } } ; eq { hop { filter_eq { all_rows ; status ; rural community } ; official name } ; beaubassin east } }, interpretation: select the row whose time record of all rows is 3rd minimum . the race record of this row is derrinstown stud derby trial .
Output: no
Command: eq { hop { nth_argmin { all_rows ; time ; 3 } ; race } ; derrinstown stud derby trial }, interpretation: select the row whose time record of all rows is 3rd minimum . the race record of this row is derrinstown stud derby trial .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; time ; 3 } ; race } ; derrinstown stud derby trial }, interpretation: select the rows whose venue record fuzzily matches to athens , greece . take the date record of this row . select the rows whose venue record fuzzily matches to oslo , norway . 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 ; venue ; athens , greece } ; date } ; hop { filter_eq { all_rows ; venue ; oslo , norway } ; date } }, interpretation: select the rows whose venue record fuzzily matches to athens , greece . take the date record of this row . select the rows whose venue record fuzzily matches to oslo , norway . 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 ; venue ; athens , greece } ; date } ; hop { filter_eq { all_rows ; venue ; oslo , norway } ; date } }, interpretation: select the rows whose year record fuzzily matches to 1961 . take the rank record of this row . select the rows whose year record fuzzily matches to 1970 . take the rank record of this row . the first record is less than the second record . the rank record of the first row is 17 . the rank record of the second row is 22 .
Output: no
Command: and { less { hop { filter_eq { all_rows ; year ; 1961 } ; rank } ; hop { filter_eq { all_rows ; year ; 1970 } ; rank } } ; and { eq { hop { filter_eq { all_rows ; year ; 1961 } ; rank } ; 17 } ; eq { hop { filter_eq { all_rows ; year ; 1970 } ; rank } ; 22 } } }, interpretation: select the rows whose year record fuzzily matches to 1961 . take the rank record of this row . select the rows whose year record fuzzily matches to 1970 . take the rank record of this row . the first record is less than the second record . the rank record of the first row is 17 . the rank record of the second row is 22 .
Output: yes
Command: and { less { hop { filter_eq { all_rows ; year ; 1961 } ; rank } ; hop { filter_eq { all_rows ; year ; 1970 } ; rank } } ; and { eq { hop { filter_eq { all_rows ; year ; 1961 } ; rank } ; 17 } ; eq { hop { filter_eq { all_rows ; year ; 1970 } ; rank } ; 22 } } }, interpretation: select the rows whose warship record fuzzily matches to chacabuco . take the speed ( knots ) record of this row . select the rows whose warship record fuzzily matches to abtao . take the speed ( knots ) record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; warship ; chacabuco } ; speed ( knots ) } ; hop { filter_eq { all_rows ; warship ; abtao } ; speed ( knots ) } }, interpretation: select the rows whose warship record fuzzily matches to chacabuco . take the speed ( knots ) record of this row . select the rows whose warship record fuzzily matches to abtao . take the speed ( knots ) record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; warship ; chacabuco } ; speed ( knots ) } ; hop { filter_eq { all_rows ; warship ; abtao } ; speed ( knots ) } }, interpretation: select the row whose matches record of all rows is maximum . the year record of this row is 2012 .
Output: no
Command: eq { hop { argmax { all_rows ; matches } ; year } ; 2012 }, interpretation: select the row whose matches record of all rows is maximum . the year record of this row is 2012 .
Output: yes
Command: eq { hop { argmax { all_rows ; matches } ; year } ; 2012 }, interpretation: for the partner records of all rows , most of them fuzzily match to jürgen melzer .
Output: no
Command: most_eq { all_rows ; partner ; jürgen melzer }, interpretation: for the partner records of all rows , most of them fuzzily match to jürgen melzer .
Output: yes
Command: most_eq { all_rows ; partner ; jürgen melzer }, interpretation: select the row whose face value record of all rows is 4th maximum . the ecosystem record of this row is alpine tundra .
Output: no
Command: eq { hop { nth_argmax { all_rows ; face value ; 4 } ; ecosystem } ; alpine tundra }, interpretation: select the row whose face value record of all rows is 4th maximum . the ecosystem record of this row is alpine tundra .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; face value ; 4 } ; ecosystem } ; alpine tundra }, interpretation: select the row whose muzzle energy record of all rows is maximum . the cartridge record of this row is .357 magnum .
Output: no
Command: eq { hop { argmax { all_rows ; muzzle energy } ; cartridge } ; .357 magnum }, interpretation: select the row whose muzzle energy record of all rows is maximum . the cartridge record of this row is .357 magnum .
Output: yes
Command: eq { hop { argmax { all_rows ; muzzle energy } ; cartridge } ; .357 magnum }, interpretation: the average of the crowd record of all rows is 21000-22000 .
Output: no
Command: round_eq { avg { all_rows ; crowd } ; 21000-22000 }, interpretation: the average of the crowd record of all rows is 21000-22000 .
Output: yes
Command: round_eq { avg { all_rows ; crowd } ; 21000-22000 }, interpretation: for the horse records of all rows , most of them fuzzily match to ypäjä karuso .
Output: no
Command: most_eq { all_rows ; horse ; ypäjä karuso }, interpretation: for the horse records of all rows , most of them fuzzily match to ypäjä karuso .
Output: yes
Command: most_eq { all_rows ; horse ; ypäjä karuso }, interpretation: select the rows whose country record fuzzily matches to united states . the average of the score record of these rows is 279.4 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; country ; united states } ; score } ; 279.4 }, interpretation: select the rows whose country record fuzzily matches to united states . the average of the score record of these rows is 279.4 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; country ; united states } ; score } ; 279.4 }, interpretation: select the rows whose position record fuzzily matches to running back . there is only one such row in the table . the player record of this unqiue row is mel farr .
Output: no
Command: and { only { filter_eq { all_rows ; position ; running back } } ; eq { hop { filter_eq { all_rows ; position ; running back } ; player } ; mel farr } }, interpretation: select the rows whose position record fuzzily matches to running back . there is only one such row in the table . the player record of this unqiue row is mel farr .
Output: yes
Command: and { only { filter_eq { all_rows ; position ; running back } } ; eq { hop { filter_eq { all_rows ; position ; running back } ; player } ; mel farr } }, interpretation: select the row whose viewers ( million ) record of all rows is maximum . the title record of this row is no excuses .
Output: no
Command: eq { hop { argmax { all_rows ; viewers ( million ) } ; title } ; no excuses }, interpretation: select the row whose viewers ( million ) record of all rows is maximum . the title record of this row is no excuses .
Output: yes
Command: eq { hop { argmax { all_rows ; viewers ( million ) } ; title } ; no excuses }, interpretation: select the rows whose 1st leg record fuzzily matches to 0-3 . there is only one such row in the table . the team 1 record of this unqiue row is blooming . the team 2 record of this unqiue row is river plate .
Output: no
Command: and { only { filter_eq { all_rows ; 1st leg ; 0-3 } } ; and { eq { hop { filter_eq { all_rows ; 1st leg ; 0-3 } ; team 1 } ; blooming } ; eq { hop { filter_eq { all_rows ; 1st leg ; 0-3 } ; team 2 } ; river plate } } }, interpretation: select the rows whose 1st leg record fuzzily matches to 0-3 . there is only one such row in the table . the team 1 record of this unqiue row is blooming . the team 2 record of this unqiue row is river plate .
Output: yes
Command: and { only { filter_eq { all_rows ; 1st leg ; 0-3 } } ; and { eq { hop { filter_eq { all_rows ; 1st leg ; 0-3 } ; team 1 } ; blooming } ; eq { hop { filter_eq { all_rows ; 1st leg ; 0-3 } ; team 2 } ; river plate } } }, interpretation: select the row whose attendance record of all rows is 1st maximum . the acc team record of this row is 4 duke .
Output: no
Command: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; acc team } ; 4 duke }, interpretation: select the row whose attendance record of all rows is 1st maximum . the acc team record of this row is 4 duke .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; acc team } ; 4 duke }, interpretation: select the rows whose position record fuzzily matches to left wing . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; position ; left wing } } ; 4 }, interpretation: select the rows whose position record fuzzily matches to left wing . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; position ; left wing } } ; 4 }, interpretation: for the date records of all rows , all of them fuzzily match to 17 june 1939 .
Output: no