niryuu/sni-each-converted huggingface dataset:
Command: eq { max { filter_eq { all_rows ; district ; fregenal de la sierra } ; election date } ; 1853 - 2 - 4 }, interpretation: select the rows whose district record fuzzily matches to fregenal de la sierra . the maximum election date record of these rows is 1853 - 2 - 4 .
Output: yes
Command: eq { max { filter_eq { all_rows ; district ; fregenal de la sierra } ; election date } ; 1853 - 2 - 4 }, interpretation: for the class records of all rows , all of them fuzzily match to d .
Output: no
Command: all_eq { all_rows ; class ; d }, interpretation: for the class records of all rows , all of them fuzzily match to d .
Output: yes
Command: all_eq { all_rows ; class ; d }, interpretation: the average of the episode record of all rows is 240 .
Output: no
Command: round_eq { avg { all_rows ; episode } ; 240 }, interpretation: the average of the episode record of all rows is 240 .
Output: yes
Command: round_eq { avg { all_rows ; episode } ; 240 }, interpretation: select the row whose game record of all rows is 3rd minimum . the team record of this row is miami .
Output: no
Command: eq { hop { nth_argmin { all_rows ; game ; 3 } ; team } ; miami }, interpretation: select the row whose game record of all rows is 3rd minimum . the team record of this row is miami .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; game ; 3 } ; team } ; miami }, interpretation: select the rows whose school / club team record fuzzily matches to maynard evans hs . there is only one such row in the table . the player record of this unqiue row is darryl dawkins .
Output: no
Command: and { only { filter_eq { all_rows ; school / club team ; maynard evans hs } } ; eq { hop { filter_eq { all_rows ; school / club team ; maynard evans hs } ; player } ; darryl dawkins } }, interpretation: select the rows whose school / club team record fuzzily matches to maynard evans hs . there is only one such row in the table . the player record of this unqiue row is darryl dawkins .
Output: yes
Command: and { only { filter_eq { all_rows ; school / club team ; maynard evans hs } } ; eq { hop { filter_eq { all_rows ; school / club team ; maynard evans hs } ; player } ; darryl dawkins } }, interpretation: select the rows whose result record fuzzily matches to loss . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; result ; loss } } ; 2 }, interpretation: select the rows whose result record fuzzily matches to loss . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; result ; loss } } ; 2 }, interpretation: select the rows whose dutch cup record fuzzily matches to finalist . for the postseason records of these rows , most of them fuzzily match to champion .
Output: no
Command: most_eq { filter_eq { all_rows ; dutch cup ; finalist } ; postseason ; champion }, interpretation: select the rows whose dutch cup record fuzzily matches to finalist . for the postseason records of these rows , most of them fuzzily match to champion .
Output: yes
Command: most_eq { filter_eq { all_rows ; dutch cup ; finalist } ; postseason ; champion }, interpretation: select the rows whose name record fuzzily matches to frank borman . take the born record of this row . select the rows whose name record fuzzily matches to jim lovell . take the born record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; name ; frank borman } ; born } ; hop { filter_eq { all_rows ; name ; jim lovell } ; born } }, interpretation: select the rows whose name record fuzzily matches to frank borman . take the born record of this row . select the rows whose name record fuzzily matches to jim lovell . take the born record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; name ; frank borman } ; born } ; hop { filter_eq { all_rows ; name ; jim lovell } ; born } }, interpretation: select the rows whose jyutping record fuzzily matches to loeng2 . take the imperial value record of this row . select the rows whose jyutping record fuzzily matches to daam3 . take the imperial value record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; jyutping ; loeng2 } ; imperial value } ; hop { filter_eq { all_rows ; jyutping ; daam3 } ; imperial value } }, interpretation: select the rows whose jyutping record fuzzily matches to loeng2 . take the imperial value record of this row . select the rows whose jyutping record fuzzily matches to daam3 . take the imperial value record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; jyutping ; loeng2 } ; imperial value } ; hop { filter_eq { all_rows ; jyutping ; daam3 } ; imperial value } }, interpretation: for the seats records of all rows , all of them are equal to 2 .
Output: no
Command: all_eq { all_rows ; seats ; 2 }, interpretation: for the seats records of all rows , all of them are equal to 2 .
Output: yes
Command: all_eq { all_rows ; seats ; 2 }, interpretation: select the row whose age on mission record of all rows is 1st maximum . the name record of this row is jim lovell .
Output: no
Command: eq { hop { nth_argmax { all_rows ; age on mission ; 1 } ; name } ; jim lovell }, interpretation: select the row whose age on mission record of all rows is 1st maximum . the name record of this row is jim lovell .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; age on mission ; 1 } ; name } ; jim lovell }, interpretation: for the color commentator ( s ) records of all rows , all of them fuzzily match to stu lantz .
Output: no
Command: all_eq { all_rows ; color commentator ( s ) ; stu lantz }, interpretation: for the color commentator ( s ) records of all rows , all of them fuzzily match to stu lantz .
Output: yes
Command: all_eq { all_rows ; color commentator ( s ) ; stu lantz }, interpretation: select the rows whose date record fuzzily matches to 27 november 1997 . take the attendance record of this row . select the rows whose date record fuzzily matches to 10 december 1997 . take the attendance record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; date ; 27 november 1997 } ; attendance } ; hop { filter_eq { all_rows ; date ; 10 december 1997 } ; attendance } }, interpretation: select the rows whose date record fuzzily matches to 27 november 1997 . take the attendance record of this row . select the rows whose date record fuzzily matches to 10 december 1997 . take the attendance record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; date ; 27 november 1997 } ; attendance } ; hop { filter_eq { all_rows ; date ; 10 december 1997 } ; attendance } }, interpretation: select the rows whose season record is arbitrary . the number of such rows is 5 .
Output: no
Command: eq { count { filter_all { all_rows ; season } } ; 5 }, interpretation: select the rows whose season record is arbitrary . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_all { all_rows ; season } } ; 5 }, interpretation: select the row whose size record of all rows is 2nd maximum . the school record of this row is southwestern hanover .
Output: no
Command: eq { hop { nth_argmax { all_rows ; size ; 2 } ; school } ; southwestern hanover }, interpretation: select the row whose size record of all rows is 2nd maximum . the school record of this row is southwestern hanover .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; size ; 2 } ; school } ; southwestern hanover }, interpretation: select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose score record is less than 208 . there is only one such row in the table .
Output: no
Command: only { filter_less { filter_eq { all_rows ; country ; united states } ; score ; 208 } }, interpretation: select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose score record is less than 208 . there is only one such row in the table .
Output: yes
Command: only { filter_less { filter_eq { all_rows ; country ; united states } ; score ; 208 } }, interpretation: select the rows whose rank record is less than or equal to 5 . for the goals records of these rows , all of them are greater than 100 .
Output: no
Command: all_greater { filter_less_eq { all_rows ; rank ; 5 } ; goals ; 100 }, interpretation: select the rows whose rank record is less than or equal to 5 . for the goals records of these rows , all of them are greater than 100 .
Output: yes
Command: all_greater { filter_less_eq { all_rows ; rank ; 5 } ; goals ; 100 }, interpretation: for the mean score records of all rows , most of them are greater than 621 .
Output: no
Command: most_greater { all_rows ; mean score ; 621 }, interpretation: for the mean score records of all rows , most of them are greater than 621 .
Output: yes
Command: most_greater { all_rows ; mean score ; 621 }, interpretation: select the rows whose years record fuzzily matches to 192 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; years ; 192 } } ; 2 }, interpretation: select the rows whose years record fuzzily matches to 192 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; years ; 192 } } ; 2 }, interpretation: select the rows whose position record fuzzily matches to rhp . the number of such rows is 6 .
Output: no
Command: eq { count { filter_eq { all_rows ; position ; rhp } } ; 6 }, interpretation: select the rows whose position record fuzzily matches to rhp . the number of such rows is 6 .
Output: yes
Command: eq { count { filter_eq { all_rows ; position ; rhp } } ; 6 }, interpretation: select the row whose us viewers ( millions ) record of all rows is maximum . the no in series record of this row is 1 .
Output: no
Command: eq { hop { argmax { all_rows ; us viewers ( millions ) } ; no in series } ; 1 }, interpretation: select the row whose us viewers ( millions ) record of all rows is maximum . the no in series record of this row is 1 .
Output: yes
Command: eq { hop { argmax { all_rows ; us viewers ( millions ) } ; no in series } ; 1 }, interpretation: select the rows whose opponent in the final record fuzzily matches to graeme dott . there is only one such row in the table . the championship record of this unqiue row is world series - berlin event .
Output: no
Command: and { only { filter_eq { all_rows ; opponent in the final ; graeme dott } } ; eq { hop { filter_eq { all_rows ; opponent in the final ; graeme dott } ; championship } ; world series - berlin event } }, interpretation: select the rows whose opponent in the final record fuzzily matches to graeme dott . there is only one such row in the table . the championship record of this unqiue row is world series - berlin event .
Output: yes
Command: and { only { filter_eq { all_rows ; opponent in the final ; graeme dott } } ; eq { hop { filter_eq { all_rows ; opponent in the final ; graeme dott } ; championship } ; world series - berlin event } }, interpretation: select the row whose bronze record of all rows is 2nd maximum . the nation record of this row is colombia .
Output: no
Command: eq { hop { nth_argmax { all_rows ; bronze ; 2 } ; nation } ; colombia }, interpretation: select the row whose bronze record of all rows is 2nd maximum . the nation record of this row is colombia .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; bronze ; 2 } ; nation } ; colombia }, interpretation: the average of the crowd record of all rows is 24303 .
Output: no
Command: round_eq { avg { all_rows ; crowd } ; 24303 }, interpretation: the average of the crowd record of all rows is 24303 .
Output: yes
Command: round_eq { avg { all_rows ; crowd } ; 24303 }, interpretation: select the rows whose high assists record fuzzily matches to rafer alston . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; high assists ; rafer alston } } ; 4 }, interpretation: select the rows whose high assists record fuzzily matches to rafer alston . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; high assists ; rafer alston } } ; 4 }, interpretation: select the rows whose lost record is equal to 0 . there is only one such row in the table . the team record of this unqiue row is são paulo .
Output: no
Command: and { only { filter_eq { all_rows ; lost ; 0 } } ; eq { hop { filter_eq { all_rows ; lost ; 0 } ; team } ; são paulo } }, interpretation: select the rows whose lost record is equal to 0 . there is only one such row in the table . the team record of this unqiue row is são paulo .
Output: yes
Command: and { only { filter_eq { all_rows ; lost ; 0 } } ; eq { hop { filter_eq { all_rows ; lost ; 0 } ; team } ; são paulo } }, interpretation: for the date records of all rows , all of them fuzzily match to march .
Output: no
Command: all_eq { all_rows ; date ; march }, interpretation: for the date records of all rows , all of them fuzzily match to march .
Output: yes
Command: all_eq { all_rows ; date ; march }, interpretation: select the rows whose directed by record fuzzily matches to steve mann . among these rows , select the rows whose written by record fuzzily matches to john ridley . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; directed by ; steve mann } ; written by ; john ridley } } ; 3 }, interpretation: select the rows whose directed by record fuzzily matches to steve mann . among these rows , select the rows whose written by record fuzzily matches to john ridley . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; directed by ; steve mann } ; written by ; john ridley } } ; 3 }, interpretation: select the rows whose date record fuzzily matches to 16 august 2000 . take the competition record of this row . select the rows whose date record fuzzily matches to 22 august 2007 . take the competition record of this row . the first record fuzzily matches to the second record . the competition record of the first row is friendly . the competition record of the second row is friendly .
Output: no
Command: and { eq { hop { filter_eq { all_rows ; date ; 16 august 2000 } ; competition } ; hop { filter_eq { all_rows ; date ; 22 august 2007 } ; competition } } ; and { eq { hop { filter_eq { all_rows ; date ; 16 august 2000 } ; competition } ; friendly } ; eq { hop { filter_eq { all_rows ; date ; 22 august 2007 } ; competition } ; friendly } } }, interpretation: select the rows whose date record fuzzily matches to 16 august 2000 . take the competition record of this row . select the rows whose date record fuzzily matches to 22 august 2007 . take the competition record of this row . the first record fuzzily matches to the second record . the competition record of the first row is friendly . the competition record of the second row is friendly .
Output: yes
Command: and { eq { hop { filter_eq { all_rows ; date ; 16 august 2000 } ; competition } ; hop { filter_eq { all_rows ; date ; 22 august 2007 } ; competition } } ; and { eq { hop { filter_eq { all_rows ; date ; 16 august 2000 } ; competition } ; friendly } ; eq { hop { filter_eq { all_rows ; date ; 22 august 2007 } ; competition } ; friendly } } }, interpretation: select the rows whose total ( kg ) record is greater than or equal to 330 . among these rows , select the rows whose snatch record is less than 150 . there is only one such row in the table . the name record of this unqiue row is juan quiterio ( dom ) .
Output: no
Command: and { only { filter_less { filter_greater_eq { all_rows ; total ( kg ) ; 330 } ; snatch ; 150 } } ; eq { hop { filter_less { filter_greater_eq { all_rows ; total ( kg ) ; 330 } ; snatch ; 150 } ; name } ; juan quiterio ( dom ) } }, interpretation: select the rows whose total ( kg ) record is greater than or equal to 330 . among these rows , select the rows whose snatch record is less than 150 . there is only one such row in the table . the name record of this unqiue row is juan quiterio ( dom ) .
Output: yes
Command: and { only { filter_less { filter_greater_eq { all_rows ; total ( kg ) ; 330 } ; snatch ; 150 } } ; eq { hop { filter_less { filter_greater_eq { all_rows ; total ( kg ) ; 330 } ; snatch ; 150 } ; name } ; juan quiterio ( dom ) } }, interpretation: select the rows whose result record fuzzily matches to draw . there is only one such row in the table . the opponent record of this unqiue row is cătălin zmărăndescu .
Output: no
Command: and { only { filter_eq { all_rows ; result ; draw } } ; eq { hop { filter_eq { all_rows ; result ; draw } ; opponent } ; cătălin zmărăndescu } }, interpretation: select the rows whose result record fuzzily matches to draw . there is only one such row in the table . the opponent record of this unqiue row is cătălin zmărăndescu .
Output: yes
Command: and { only { filter_eq { all_rows ; result ; draw } } ; eq { hop { filter_eq { all_rows ; result ; draw } ; opponent } ; cătălin zmărăndescu } }, interpretation: select the rows whose race record fuzzily matches to german grand prix . take the date record of this row . select the rows whose race record fuzzily matches to british grand prix . take the date record of this row . the first record is 15 days larger than the second record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; race ; german grand prix } ; date } ; hop { filter_eq { all_rows ; race ; british grand prix } ; date } } ; 15 days }, interpretation: select the rows whose race record fuzzily matches to german grand prix . take the date record of this row . select the rows whose race record fuzzily matches to british grand prix . take the date record of this row . the first record is 15 days larger than the second record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; race ; german grand prix } ; date } ; hop { filter_eq { all_rows ; race ; british grand prix } ; date } } ; 15 days }, interpretation: select the rows whose name record fuzzily matches to kseniya moskvina . take the time record of this row . select the rows whose name record fuzzily matches to sophie edington . take the time record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; name ; kseniya moskvina } ; time } ; hop { filter_eq { all_rows ; name ; sophie edington } ; time } }, interpretation: select the rows whose name record fuzzily matches to kseniya moskvina . take the time record of this row . select the rows whose name record fuzzily matches to sophie edington . take the time record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; name ; kseniya moskvina } ; time } ; hop { filter_eq { all_rows ; name ; sophie edington } ; time } }, interpretation: select the rows whose date of reclassification record fuzzily matches to merge into shizuoka . there is only one such row in the table . the name record of this unqiue row is shimizu .
Output: no
Command: and { only { filter_eq { all_rows ; date of reclassification ; merge into shizuoka } } ; eq { hop { filter_eq { all_rows ; date of reclassification ; merge into shizuoka } ; name } ; shimizu } }, interpretation: select the rows whose date of reclassification record fuzzily matches to merge into shizuoka . there is only one such row in the table . the name record of this unqiue row is shimizu .
Output: yes
Command: and { only { filter_eq { all_rows ; date of reclassification ; merge into shizuoka } } ; eq { hop { filter_eq { all_rows ; date of reclassification ; merge into shizuoka } ; name } ; shimizu } }, interpretation: for the region records of all rows , most of them fuzzily match to usa .
Output: no
Command: most_eq { all_rows ; region ; usa }, interpretation: for the region records of all rows , most of them fuzzily match to usa .
Output: yes
Command: most_eq { all_rows ; region ; usa }, interpretation: select the rows whose opponent record fuzzily matches to richard white . take the round record of this row . select the rows whose opponent record fuzzily matches to jeremy beck . take the round record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; opponent ; richard white } ; round } ; hop { filter_eq { all_rows ; opponent ; jeremy beck } ; round } }, interpretation: select the rows whose opponent record fuzzily matches to richard white . take the round record of this row . select the rows whose opponent record fuzzily matches to jeremy beck . take the round record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; opponent ; richard white } ; round } ; hop { filter_eq { all_rows ; opponent ; jeremy beck } ; round } }, interpretation: the average of the attendance record of all rows is 21273 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 21273 }, interpretation: the average of the attendance record of all rows is 21273 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 21273 }, interpretation: select the rows whose result record fuzzily matches to retired jacksonian hold . there is only one such row in the table . the incumbent record of this unqiue row is john floyd .
Output: no
Command: and { only { filter_eq { all_rows ; result ; retired jacksonian hold } } ; eq { hop { filter_eq { all_rows ; result ; retired jacksonian hold } ; incumbent } ; john floyd } }, interpretation: select the rows whose result record fuzzily matches to retired jacksonian hold . there is only one such row in the table . the incumbent record of this unqiue row is john floyd .
Output: yes
Command: and { only { filter_eq { all_rows ; result ; retired jacksonian hold } } ; eq { hop { filter_eq { all_rows ; result ; retired jacksonian hold } ; incumbent } ; john floyd } }, interpretation: select the rows whose nationality record fuzzily matches to bosnia and herzegovina . there is only one such row in the table . the previous team record of this unqiue row is golden state warriors .
Output: no
Command: and { only { filter_eq { all_rows ; nationality ; bosnia and herzegovina } } ; eq { hop { filter_eq { all_rows ; nationality ; bosnia and herzegovina } ; previous team } ; golden state warriors } }, interpretation: select the rows whose nationality record fuzzily matches to bosnia and herzegovina . there is only one such row in the table . the previous team record of this unqiue row is golden state warriors .
Output: yes
Command: and { only { filter_eq { all_rows ; nationality ; bosnia and herzegovina } } ; eq { hop { filter_eq { all_rows ; nationality ; bosnia and herzegovina } ; previous team } ; golden state warriors } }, interpretation: select the row whose place record of all rows is minimum . the player record of this row is davis love iii .
Output: no
Command: eq { hop { argmin { all_rows ; place } ; player } ; davis love iii }, interpretation: select the row whose place record of all rows is minimum . the player record of this row is davis love iii .
Output: yes
Command: eq { hop { argmin { all_rows ; place } ; player } ; davis love iii }, interpretation: for the % ( 2000 ) records of all rows , most of them are less than 6 .
Output: no
Command: most_less { all_rows ; % ( 2000 ) ; 6 }, interpretation: for the % ( 2000 ) records of all rows , most of them are less than 6 .
Output: yes
Command: most_less { all_rows ; % ( 2000 ) ; 6 }, interpretation: select the rows whose platform ( s ) record fuzzily matches to gamecube . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; platform ( s ) ; gamecube } } ; 5 }, interpretation: select the rows whose platform ( s ) record fuzzily matches to gamecube . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { all_rows ; platform ( s ) ; gamecube } } ; 5 }, interpretation: the sum of the production in 2011 ( 1000 ton ) record of all rows is 290,133 .
Output: no
Command: round_eq { sum { all_rows ; production in 2011 ( 1000 ton ) } ; 290,133 }, interpretation: the sum of the production in 2011 ( 1000 ton ) record of all rows is 290,133 .
Output: yes
Command: round_eq { sum { all_rows ; production in 2011 ( 1000 ton ) } ; 290,133 }, interpretation: select the rows whose division record record fuzzily matches to 4-2 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; division record ; 4-2 } } ; 2 }, interpretation: select the rows whose division record record fuzzily matches to 4-2 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; division record ; 4-2 } } ; 2 }, interpretation: the average of the viewers ( in millions ) record of all rows is 7.76 .
Output: no
Command: round_eq { avg { all_rows ; viewers ( in millions ) } ; 7.76 }, interpretation: the average of the viewers ( in millions ) record of all rows is 7.76 .
Output: yes
Command: round_eq { avg { all_rows ; viewers ( in millions ) } ; 7.76 }, interpretation: the average of the area ( km square ) record of all rows is 47 .
Output: no
Command: round_eq { avg { all_rows ; area ( km square ) } ; 47 }, interpretation: the average of the area ( km square ) record of all rows is 47 .
Output: yes
Command: round_eq { avg { all_rows ; area ( km square ) } ; 47 }, interpretation: select the rows whose written by record fuzzily matches to robert king & michelle king . the average of the us viewers ( million ) record of these rows is 12.47 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; written by ; robert king & michelle king } ; us viewers ( million ) } ; 12.47 }, interpretation: select the rows whose written by record fuzzily matches to robert king & michelle king . the average of the us viewers ( million ) record of these rows is 12.47 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; written by ; robert king & michelle king } ; us viewers ( million ) } ; 12.47 }, interpretation: the average of the age record of all rows is 25.6 .
Output: no
Command: round_eq { avg { all_rows ; age } ; 25.6 }, interpretation: the average of the age record of all rows is 25.6 .
Output: yes
Command: round_eq { avg { all_rows ; age } ; 25.6 }, interpretation: select the row whose length record of all rows is maximum . the name record of this row is northern eagle .
Output: no
Command: eq { hop { argmax { all_rows ; length } ; name } ; northern eagle }, interpretation: select the row whose length record of all rows is maximum . the name record of this row is northern eagle .
Output: yes
Command: eq { hop { argmax { all_rows ; length } ; name } ; northern eagle }, interpretation: select the rows whose state / territory record fuzzily matches to queensland . the number of such rows is 6 .
Output: no
Command: eq { count { filter_eq { all_rows ; state / territory ; queensland } } ; 6 }, interpretation: select the rows whose state / territory record fuzzily matches to queensland . the number of such rows is 6 .
Output: yes
Command: eq { count { filter_eq { all_rows ; state / territory ; queensland } } ; 6 }, interpretation: for the played records of all rows , all of them are equal to 30 .
Output: no
Command: all_eq { all_rows ; played ; 30 }, interpretation: select the rows whose first elected record is equal to 1793 . there is only one such row in the table . the incumbent record of this unqiue row is john nicholas .
Output: no
Command: and { only { filter_eq { all_rows ; first elected ; 1793 } } ; eq { hop { filter_eq { all_rows ; first elected ; 1793 } ; incumbent } ; john nicholas } }, interpretation: select the rows whose first elected record is equal to 1793 . there is only one such row in the table . the incumbent record of this unqiue row is john nicholas .
Output: yes
Command: and { only { filter_eq { all_rows ; first elected ; 1793 } } ; eq { hop { filter_eq { all_rows ; first elected ; 1793 } ; incumbent } ; john nicholas } }, interpretation: the average of the points record of all rows is 48.8 .
Output: no
Command: round_eq { avg { all_rows ; points } ; 48.8 }, interpretation: the average of the points record of all rows is 48.8 .
Output: yes
Command: round_eq { avg { all_rows ; points } ; 48.8 }, interpretation: select the row whose quantity record of all rows is maximum . the make record of this row is gm new look .
Output: no
Command: eq { hop { argmax { all_rows ; quantity } ; make } ; gm new look }, interpretation: select the row whose quantity record of all rows is maximum . the make record of this row is gm new look .
Output: yes
Command: eq { hop { argmax { all_rows ; quantity } ; make } ; gm new look }, interpretation: for the competition records of all rows , most of them fuzzily match to 1908 olympics .
Output: no
Command: most_eq { all_rows ; competition ; 1908 olympics }, interpretation: for the competition records of all rows , most of them fuzzily match to 1908 olympics .
Output: yes
Command: most_eq { all_rows ; competition ; 1908 olympics }, interpretation: select the rows whose home record fuzzily matches to minnesota . among these rows , select the rows whose attendance record is greater than or equal to 19360 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_greater_eq { filter_eq { all_rows ; home ; minnesota } ; attendance ; 19360 } } ; 2 }, interpretation: select the rows whose home record fuzzily matches to minnesota . among these rows , select the rows whose attendance record is greater than or equal to 19360 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_greater_eq { filter_eq { all_rows ; home ; minnesota } ; attendance ; 19360 } } ; 2 }, interpretation: select the row whose points per game record of all rows is minimum . the tournament record of this row is 2011 eurobasket .
Output: no
Command: eq { hop { argmin { all_rows ; points per game } ; tournament } ; 2011 eurobasket }, interpretation: select the row whose points per game record of all rows is minimum . the tournament record of this row is 2011 eurobasket .
Output: yes
Command: eq { hop { argmin { all_rows ; points per game } ; tournament } ; 2011 eurobasket }, interpretation: for the original air date records of all rows , most of them fuzzily match to 2008 .
Output: no
Command: most_eq { all_rows ; original air date ; 2008 }, interpretation: for the original air date records of all rows , most of them fuzzily match to 2008 .
Output: yes
Command: most_eq { all_rows ; original air date ; 2008 }, interpretation: select the row whose jump 1 record of all rows is 2nd maximum . the athlete name record of this row is dmitriy bondarenko ( urs ) .
Output: no
Command: eq { hop { nth_argmax { all_rows ; jump 1 ; 2 } ; athlete name } ; dmitriy bondarenko ( urs ) }, interpretation: select the row whose jump 1 record of all rows is 2nd maximum . the athlete name record of this row is dmitriy bondarenko ( urs ) .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; jump 1 ; 2 } ; athlete name } ; dmitriy bondarenko ( urs ) }, interpretation: select the rows whose manager record fuzzily matches to bob didier . there is only one such row in the table . the year record of this unqiue row is 1977 .
Output: no
Command: and { only { filter_eq { all_rows ; manager ; bob didier } } ; eq { hop { filter_eq { all_rows ; manager ; bob didier } ; year } ; 1977 } }, interpretation: select the rows whose manager record fuzzily matches to bob didier . there is only one such row in the table . the year record of this unqiue row is 1977 .
Output: yes
Command: and { only { filter_eq { all_rows ; manager ; bob didier } } ; eq { hop { filter_eq { all_rows ; manager ; bob didier } ; year } ; 1977 } }, interpretation: select the rows whose oberbayern record fuzzily matches to fc ingolstadt 04 . the minimum season record of these rows is 2005 .
Output: no
Command: eq { min { filter_eq { all_rows ; oberbayern ; fc ingolstadt 04 } ; season } ; 2005 }, interpretation: select the rows whose oberbayern record fuzzily matches to fc ingolstadt 04 . the minimum season record of these rows is 2005 .
Output: yes
Command: eq { min { filter_eq { all_rows ; oberbayern ; fc ingolstadt 04 } ; season } ; 2005 }, interpretation: select the rows whose aggregate score record fuzzily matches to 0 . there is only one such row in the table . the opposition record of this unqiue row is fc köln .
Output: no
Command: and { only { filter_eq { all_rows ; aggregate score ; 0 } } ; eq { hop { filter_eq { all_rows ; aggregate score ; 0 } ; opposition } ; fc köln } }, interpretation: select the rows whose aggregate score record fuzzily matches to 0 . there is only one such row in the table . the opposition record of this unqiue row is fc köln .
Output: yes
Command: and { only { filter_eq { all_rows ; aggregate score ; 0 } } ; eq { hop { filter_eq { all_rows ; aggregate score ; 0 } ; opposition } ; fc köln } }, interpretation: select the rows whose location attendance record fuzzily matches to rose garden . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; location attendance ; rose garden } } ; 3 }, interpretation: select the rows whose location attendance record fuzzily matches to rose garden . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; location attendance ; rose garden } } ; 3 }, interpretation: select the rows whose away team score record is less than 10 . the number of such rows is 1 .
Output: no
Command: eq { count { filter_less { all_rows ; away team score ; 10 } } ; 1 }, interpretation: select the rows whose away team score record is less than 10 . the number of such rows is 1 .
Output: yes
Command: eq { count { filter_less { all_rows ; away team score ; 10 } } ; 1 }, interpretation: select the row whose date record of all rows is 3rd minimum . the venue record of this row is victoria park . the crowd record of this row is 23000 .
Output: no
Command: and { eq { hop { nth_argmin { all_rows ; date ; 3 } ; venue } ; victoria park } ; eq { hop { nth_argmin { all_rows ; date ; 3 } ; crowd } ; 23000 } }, interpretation: select the row whose date record of all rows is 3rd minimum . the venue record of this row is victoria park . the crowd record of this row is 23000 .
Output: yes
Command: and { eq { hop { nth_argmin { all_rows ; date ; 3 } ; venue } ; victoria park } ; eq { hop { nth_argmin { all_rows ; date ; 3 } ; crowd } ; 23000 } }, interpretation: select the rows whose round winner record fuzzily matches to larry perkins . take the date record of this row . select the rows whose round winner record fuzzily matches to mark skaife . 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 ; round winner ; larry perkins } ; date } ; hop { filter_eq { all_rows ; round winner ; mark skaife } ; date } }, interpretation: select the rows whose round winner record fuzzily matches to larry perkins . take the date record of this row . select the rows whose round winner record fuzzily matches to mark skaife . 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 ; round winner ; larry perkins } ; date } ; hop { filter_eq { all_rows ; round winner ; mark skaife } ; date } }, interpretation: select the rows whose seasons at this level record fuzzily matches to 27 seasons . for the settlements records of these rows , most of them fuzzily match to lisbon .
Output: no
Command: most_eq { filter_eq { all_rows ; seasons at this level ; 27 seasons } ; settlements ; lisbon }, interpretation: select the rows whose seasons at this level record fuzzily matches to 27 seasons . for the settlements records of these rows , most of them fuzzily match to lisbon .
Output: yes
Command: most_eq { filter_eq { all_rows ; seasons at this level ; 27 seasons } ; settlements ; lisbon }, interpretation: for the party records of all rows , all of them fuzzily match to democratic .
Output: no
Command: all_eq { all_rows ; party ; democratic }, interpretation: select the row whose elevation ( m ) record of all rows is 2nd maximum . the peak record of this row is moldoveanu peak .
Output: no
Command: eq { hop { nth_argmax { all_rows ; elevation ( m ) ; 2 } ; peak } ; moldoveanu peak }, interpretation: select the row whose elevation ( m ) record of all rows is 2nd maximum . the peak record of this row is moldoveanu peak .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; elevation ( m ) ; 2 } ; peak } ; moldoveanu peak }, interpretation: select the rows whose venue record fuzzily matches to punt road oval . take the crowd record of this row . select the rows whose venue record fuzzily matches to junction oval . take the crowd record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; venue ; punt road oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; junction oval } ; crowd } }, interpretation: select the rows whose venue record fuzzily matches to punt road oval . take the crowd record of this row . select the rows whose venue record fuzzily matches to junction oval . 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 ; punt road oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; junction oval } ; crowd } }, interpretation: select the rows whose successor record fuzzily matches to thomas tillinghast ( f ) . take the date successor seated record of this row . select the rows whose successor record fuzzily matches to robert waln ( f ) . take the date successor seated record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; successor ; thomas tillinghast ( f ) } ; date successor seated } ; hop { filter_eq { all_rows ; successor ; robert waln ( f ) } ; date successor seated } }, interpretation: select the rows whose successor record fuzzily matches to thomas tillinghast ( f ) . take the date successor seated record of this row . select the rows whose successor record fuzzily matches to robert waln ( f ) . take the date successor seated record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; successor ; thomas tillinghast ( f ) } ; date successor seated } ; hop { filter_eq { all_rows ; successor ; robert waln ( f ) } ; date successor seated } }, interpretation: select the rows whose opponent record fuzzily matches to indianapolis colts . the sum of the attendance record of these rows is 100807 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; opponent ; indianapolis colts } ; attendance } ; 100807 }, interpretation: select the rows whose opponent record fuzzily matches to indianapolis colts . the sum of the attendance record of these rows is 100807 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; opponent ; indianapolis colts } ; attendance } ; 100807 }, interpretation: select the rows whose game site record fuzzily matches to olympic stadium . there is only one such row in the table . the date record of this unqiue row is sunday , april 4 .
Output: no
Command: and { only { filter_eq { all_rows ; game site ; olympic stadium } } ; eq { hop { filter_eq { all_rows ; game site ; olympic stadium } ; date } ; sunday , april 4 } }, interpretation: select the rows whose game site record fuzzily matches to olympic stadium . there is only one such row in the table . the date record of this unqiue row is sunday , april 4 .
Output: yes
Command: and { only { filter_eq { all_rows ; game site ; olympic stadium } } ; eq { hop { filter_eq { all_rows ; game site ; olympic stadium } ; date } ; sunday , april 4 } }, interpretation: select the rows whose school / club team record fuzzily matches to duke . there is only one such row in the table . the player record of this unqiue row is antonio lang .
Output: no
Command: and { only { filter_eq { all_rows ; school / club team ; duke } } ; eq { hop { filter_eq { all_rows ; school / club team ; duke } ; player } ; antonio lang } }, interpretation: select the rows whose school / club team record fuzzily matches to duke . there is only one such row in the table . the player record of this unqiue row is antonio lang .
Output: yes
Command: and { only { filter_eq { all_rows ; school / club team ; duke } } ; eq { hop { filter_eq { all_rows ; school / club team ; duke } ; player } ; antonio lang } }, interpretation: select the rows whose position record fuzzily matches to 8th . there is only one such row in the table . the season record of this unqiue row is 1946 - 47 .
Output: no
Command: and { only { filter_eq { all_rows ; position ; 8th } } ; eq { hop { filter_eq { all_rows ; position ; 8th } ; season } ; 1946 - 47 } }, interpretation: select the rows whose position record fuzzily matches to 8th . there is only one such row in the table . the season record of this unqiue row is 1946 - 47 .
Output: yes
Command: and { only { filter_eq { all_rows ; position ; 8th } } ; eq { hop { filter_eq { all_rows ; position ; 8th } ; season } ; 1946 - 47 } }, interpretation: the average of the attendance record of all rows is 48121 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 48121 }, interpretation: the average of the attendance record of all rows is 48121 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 48121 }, interpretation: select the rows whose date record fuzzily matches to may . there is only one such row in the table .
Output: no
Command: only { filter_eq { all_rows ; date ; may } }, interpretation: select the rows whose date record fuzzily matches to may . there is only one such row in the table .
Output: yes
Command: only { filter_eq { all_rows ; date ; may } }, interpretation: select the rows whose city record fuzzily matches to barisal . take the country record of this row . select the rows whose city record fuzzily matches to chittagong . take the country record of this row . the first record fuzzily matches to the second record . the country record of the first row is bangladesh . the country record of the second row is bangladesh .
Output: no
Command: and { eq { hop { filter_eq { all_rows ; city ; barisal } ; country } ; hop { filter_eq { all_rows ; city ; chittagong } ; country } } ; and { eq { hop { filter_eq { all_rows ; city ; barisal } ; country } ; bangladesh } ; eq { hop { filter_eq { all_rows ; city ; chittagong } ; country } ; bangladesh } } }, interpretation: select the rows whose city record fuzzily matches to barisal . take the country record of this row . select the rows whose city record fuzzily matches to chittagong . take the country record of this row . the first record fuzzily matches to the second record . the country record of the first row is bangladesh . the country record of the second row is bangladesh .
Output: yes
Command: and { eq { hop { filter_eq { all_rows ; city ; barisal } ; country } ; hop { filter_eq { all_rows ; city ; chittagong } ; country } } ; and { eq { hop { filter_eq { all_rows ; city ; barisal } ; country } ; bangladesh } ; eq { hop { filter_eq { all_rows ; city ; chittagong } ; country } ; bangladesh } } }, interpretation: select the row whose born - died record of all rows is 2nd minimum . the name record of this row is sali berisha .
Output: no
Command: eq { hop { nth_argmin { all_rows ; born - died ; 2 } ; name } ; sali berisha }, interpretation: select the row whose born - died record of all rows is 2nd minimum . the name record of this row is sali berisha .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; born - died ; 2 } ; name } ; sali berisha }, interpretation: select the rows whose episode count record is greater than 80 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_greater { all_rows ; episode count ; 80 } } ; 3 }, interpretation: select the rows whose episode count record is greater than 80 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_greater { all_rows ; episode count ; 80 } } ; 3 }, interpretation: select the rows whose directed by record fuzzily matches to jeremy podeswa . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; directed by ; jeremy podeswa } } ; 2 }, interpretation: select the rows whose directed by record fuzzily matches to jeremy podeswa . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; directed by ; jeremy podeswa } } ; 2 }, interpretation: for the date records of all rows , most of them fuzzily match to may .
Output: no
Command: most_eq { all_rows ; date ; may }, interpretation: for the date records of all rows , most of them fuzzily match to may .
Output: yes
Command: most_eq { all_rows ; date ; may }, interpretation: select the rows whose kiplinger 's top 100 values record fuzzily matches to n / a . the number of such rows is 6 .
Output: no
Command: eq { count { filter_eq { all_rows ; kiplinger 's top 100 values ; n / a } } ; 6 }, interpretation: select the rows whose kiplinger 's top 100 values record fuzzily matches to n / a . the number of such rows is 6 .
Output: yes
Command: eq { count { filter_eq { all_rows ; kiplinger 's top 100 values ; n / a } } ; 6 }, interpretation: select the row whose yards record of all rows is 2nd maximum . the season record of this row is 2009 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; yards ; 2 } ; season } ; 2009 }, interpretation: select the row whose yards record of all rows is 2nd maximum . the season record of this row is 2009 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; yards ; 2 } ; season } ; 2009 }, interpretation: select the rows whose incumbent record fuzzily matches to noah m mason . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to sid simpson . 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 ; noah m mason } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; sid simpson } ; first elected } }, interpretation: select the rows whose incumbent record fuzzily matches to noah m mason . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to sid simpson . 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 ; noah m mason } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; sid simpson } ; first elected } }, interpretation: select the rows whose team record fuzzily matches to usa . the sum of the final record of these rows is 15:49.38 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; team ; usa } ; final } ; 15:49.38 }, interpretation: select the rows whose team record fuzzily matches to usa . the sum of the final record of these rows is 15:49.38 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; team ; usa } ; final } ; 15:49.38 }, interpretation: select the rows whose tournament record fuzzily matches to us open . take the cuts made record of this row . select the rows whose tournament record fuzzily matches to pga championship . take the cuts made record of this row . the first record is equal to the second record .
Output: no
Command: eq { hop { filter_eq { all_rows ; tournament ; us open } ; cuts made } ; hop { filter_eq { all_rows ; tournament ; pga championship } ; cuts made } }, interpretation: select the rows whose tournament record fuzzily matches to us open . take the cuts made record of this row . select the rows whose tournament record fuzzily matches to pga championship . take the cuts made record of this row . the first record is equal to the second record .
Output: yes
Command: eq { hop { filter_eq { all_rows ; tournament ; us open } ; cuts made } ; hop { filter_eq { all_rows ; tournament ; pga championship } ; cuts made } }, interpretation: for the width records of all rows , all of them are equal to 96 .
Output: no
Command: all_eq { all_rows ; width ; 96 }, interpretation: for the width records of all rows , all of them are equal to 96 .
Output: yes
Command: all_eq { all_rows ; width ; 96 }, interpretation: select the row whose votes record of all rows is maximum . the election record of this row is 1990 .
Output: no