niryuu/sni-each-converted huggingface dataset:
Command: and { greater { hop { filter_eq { all_rows ; home team ; fitzroy } ; crowd } ; hop { filter_eq { all_rows ; home team ; geelong } ; crowd } } ; and { eq { hop { filter_eq { all_rows ; home team ; fitzroy } ; away team } ; footscray } ; eq { hop { filter_eq { all_rows ; home team ; geelong } ; away team } ; st kilda } } }, interpretation: select the rows whose home team record fuzzily matches to fitzroy . take the crowd record of this row . select the rows whose home team record fuzzily matches to geelong . take the crowd record of this row . the first record is greater than the second record . the away team record of the first row is footscray . the away team record of the second row is st kilda .
Output: yes
Command: and { greater { hop { filter_eq { all_rows ; home team ; fitzroy } ; crowd } ; hop { filter_eq { all_rows ; home team ; geelong } ; crowd } } ; and { eq { hop { filter_eq { all_rows ; home team ; fitzroy } ; away team } ; footscray } ; eq { hop { filter_eq { all_rows ; home team ; geelong } ; away team } ; st kilda } } }, interpretation: for the transfer window records of all rows , all of them fuzzily match to winter .
Output: no
Command: all_eq { all_rows ; transfer window ; winter }, interpretation: for the transfer window records of all rows , all of them fuzzily match to winter .
Output: yes
Command: all_eq { all_rows ; transfer window ; winter }, interpretation: select the rows whose first broadcast record fuzzily matches to october . among these rows , select the rows whose seans team record fuzzily matches to joe wilkinson . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; first broadcast ; october } ; seans team ; joe wilkinson } } ; 2 }, interpretation: select the rows whose first broadcast record fuzzily matches to october . among these rows , select the rows whose seans team record fuzzily matches to joe wilkinson . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; first broadcast ; october } ; seans team ; joe wilkinson } } ; 2 }, interpretation: select the rows whose season record is equal to 2011 . the maximum races record of these rows is 18 .
Output: no
Command: eq { max { filter_eq { all_rows ; season ; 2011 } ; races } ; 18 }, interpretation: select the rows whose season record is equal to 2011 . the maximum races record of these rows is 18 .
Output: yes
Command: eq { max { filter_eq { all_rows ; season ; 2011 } ; races } ; 18 }, interpretation: select the rows whose 1992 - 93 record is equal to 37 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; 1992 - 93 ; 37 } } ; 2 }, interpretation: select the rows whose 1992 - 93 record is equal to 37 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; 1992 - 93 ; 37 } } ; 2 }, interpretation: select the rows whose date record fuzzily matches to december . among these rows , select the rows whose game site record fuzzily matches to rich stadium . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; date ; december } ; game site ; rich stadium } } ; 2 }, interpretation: select the rows whose date record fuzzily matches to december . among these rows , select the rows whose game site record fuzzily matches to rich stadium . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; date ; december } ; game site ; rich stadium } } ; 2 }, interpretation: select the row whose score record of all rows is 1st maximum . the ground record of this row is humber college north .
Output: no
Command: eq { hop { nth_argmax { all_rows ; score ; 1 } ; ground } ; humber college north }, interpretation: select the row whose score record of all rows is 1st maximum . the ground record of this row is humber college north .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; score ; 1 } ; ground } ; humber college north }, interpretation: for the surface records of all rows , most of them fuzzily match to clay .
Output: no
Command: most_eq { all_rows ; surface ; clay }, interpretation: the sum of the number of reviews record of all rows is 83 .
Output: no
Command: round_eq { sum { all_rows ; number of reviews } ; 83 }, interpretation: the sum of the number of reviews record of all rows is 83 .
Output: yes
Command: round_eq { sum { all_rows ; number of reviews } ; 83 }, interpretation: select the row whose date record of all rows is 17th minimum . the competition record of this row is 2012 philippine peace cup .
Output: no
Command: eq { hop { nth_argmin { all_rows ; date ; 17 } ; competition } ; 2012 philippine peace cup }, interpretation: select the row whose date record of all rows is 17th minimum . the competition record of this row is 2012 philippine peace cup .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; date ; 17 } ; competition } ; 2012 philippine peace cup }, interpretation: select the rows whose party record fuzzily matches to democratic . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; party ; democratic } } ; 2 }, interpretation: select the rows whose party record fuzzily matches to democratic . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; party ; democratic } } ; 2 }, interpretation: for the final score records of all rows , most of them fuzzily match to l .
Output: no
Command: most_eq { all_rows ; final score ; l }, interpretation: for the final score records of all rows , most of them fuzzily match to l .
Output: yes
Command: most_eq { all_rows ; final score ; l }, interpretation: the sum of the races record of all rows is 164 .
Output: no
Command: round_eq { sum { all_rows ; races } ; 164 }, interpretation: the sum of the races record of all rows is 164 .
Output: yes
Command: round_eq { sum { all_rows ; races } ; 164 }, interpretation: the average of the laps record of all rows is 102.9 .
Output: no
Command: round_eq { avg { all_rows ; laps } ; 102.9 }, interpretation: the average of the laps record of all rows is 102.9 .
Output: yes
Command: round_eq { avg { all_rows ; laps } ; 102.9 }, interpretation: for the res records of all rows , most of them fuzzily match to win .
Output: no
Command: most_eq { all_rows ; res ; win }, interpretation: for the res records of all rows , most of them fuzzily match to win .
Output: yes
Command: most_eq { all_rows ; res ; win }, interpretation: select the rows whose original air date record fuzzily matches to june . select the row whose us viewers ( million ) record of these rows is 1st maximum . the title record of this row is begin the begin .
Output: no
Command: eq { hop { nth_argmax { filter_eq { all_rows ; original air date ; june } ; us viewers ( million ) ; 1 } ; title } ; begin the begin }, interpretation: select the rows whose original air date record fuzzily matches to june . select the row whose us viewers ( million ) record of these rows is 1st maximum . the title record of this row is begin the begin .
Output: yes
Command: eq { hop { nth_argmax { filter_eq { all_rows ; original air date ; june } ; us viewers ( million ) ; 1 } ; title } ; begin the begin }, interpretation: select the rows whose bank record fuzzily matches to mellat bank . take the total assets ( score ) record of this row . select the rows whose bank record fuzzily matches to bank of industry and mine . take the total assets ( score ) record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; bank ; mellat bank } ; total assets ( score ) } ; hop { filter_eq { all_rows ; bank ; bank of industry and mine } ; total assets ( score ) } }, interpretation: select the rows whose bank record fuzzily matches to mellat bank . take the total assets ( score ) record of this row . select the rows whose bank record fuzzily matches to bank of industry and mine . take the total assets ( score ) record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; bank ; mellat bank } ; total assets ( score ) } ; hop { filter_eq { all_rows ; bank ; bank of industry and mine } ; total assets ( score ) } }, interpretation: select the rows whose name record fuzzily matches to alfred pfaff . take the goals record of this row . select the rows whose name record fuzzily matches to lothar schämer . take the goals record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; name ; alfred pfaff } ; goals } ; hop { filter_eq { all_rows ; name ; lothar schämer } ; goals } }, interpretation: select the rows whose name record fuzzily matches to alfred pfaff . take the goals record of this row . select the rows whose name record fuzzily matches to lothar schämer . take the goals record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; name ; alfred pfaff } ; goals } ; hop { filter_eq { all_rows ; name ; lothar schämer } ; goals } }, interpretation: the sum of the frequency mhz record of all rows is 724 .
Output: no
Command: round_eq { sum { all_rows ; frequency mhz } ; 724 }, interpretation: the sum of the frequency mhz record of all rows is 724 .
Output: yes
Command: round_eq { sum { all_rows ; frequency mhz } ; 724 }, interpretation: select the rows whose nation record is arbitrary . the number of such rows is 5 .
Output: no
Command: eq { count { filter_all { all_rows ; nation } } ; 5 }, 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 name record fuzzily matches to jeff thomson . take the matches record of this row . select the rows whose name record fuzzily matches to mitchell johnson . take the matches record of this row . the first record is 1 larger than the second record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; name ; jeff thomson } ; matches } ; hop { filter_eq { all_rows ; name ; mitchell johnson } ; matches } } ; 1 }, interpretation: select the rows whose name record fuzzily matches to jeff thomson . take the matches record of this row . select the rows whose name record fuzzily matches to mitchell johnson . take the matches record of this row . the first record is 1 larger than the second record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; name ; jeff thomson } ; matches } ; hop { filter_eq { all_rows ; name ; mitchell johnson } ; matches } } ; 1 }, interpretation: select the rows whose elevation ( m ) record is greater than 3000 . for the prominence ( m ) records of these rows , all of them are greater than 2400 .
Output: no
Command: all_greater { filter_greater { all_rows ; elevation ( m ) ; 3000 } ; prominence ( m ) ; 2400 }, interpretation: select the rows whose elevation ( m ) record is greater than 3000 . for the prominence ( m ) records of these rows , all of them are greater than 2400 .
Output: yes
Command: all_greater { filter_greater { all_rows ; elevation ( m ) ; 3000 } ; prominence ( m ) ; 2400 }, interpretation: select the row whose extra points record of all rows is maximum . the player record of this row is merv pregulman .
Output: no
Command: eq { hop { argmax { all_rows ; extra points } ; player } ; merv pregulman }, interpretation: select the row whose extra points record of all rows is maximum . the player record of this row is merv pregulman .
Output: yes
Command: eq { hop { argmax { all_rows ; extra points } ; player } ; merv pregulman }, interpretation: select the row whose laps record of all rows is maximum . the year record of this row is 2003 .
Output: no
Command: eq { hop { argmax { all_rows ; laps } ; year } ; 2003 }, interpretation: select the row whose laps record of all rows is maximum . the year record of this row is 2003 .
Output: yes
Command: eq { hop { argmax { all_rows ; laps } ; year } ; 2003 }, interpretation: select the row whose total record of all rows is maximum . the country record of this row is france .
Output: no
Command: eq { hop { argmax { all_rows ; total } ; country } ; france }, interpretation: select the row whose total record of all rows is maximum . the country record of this row is france .
Output: yes
Command: eq { hop { argmax { all_rows ; total } ; country } ; france }, interpretation: select the row whose rank record of all rows is minimum . the nation record of this row is soviet union .
Output: no
Command: eq { hop { argmin { all_rows ; rank } ; nation } ; soviet union }, interpretation: select the row whose rank record of all rows is minimum . the nation record of this row is soviet union .
Output: yes
Command: eq { hop { argmin { all_rows ; rank } ; nation } ; soviet union }, interpretation: select the rows whose artist record fuzzily matches to dire straits . take the claimed sales record of this row . select the rows whose artist record fuzzily matches to barbra streisand . take the claimed sales record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; artist ; dire straits } ; claimed sales } ; hop { filter_eq { all_rows ; artist ; barbra streisand } ; claimed sales } }, interpretation: select the rows whose artist record fuzzily matches to dire straits . take the claimed sales record of this row . select the rows whose artist record fuzzily matches to barbra streisand . take the claimed sales record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; artist ; dire straits } ; claimed sales } ; hop { filter_eq { all_rows ; artist ; barbra streisand } ; claimed sales } }, interpretation: select the rows whose party record fuzzily matches to conservative . take the votes record of this row . select the rows whose party record fuzzily matches to labour . take the votes record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; party ; conservative } ; votes } ; hop { filter_eq { all_rows ; party ; labour } ; votes } }, interpretation: select the rows whose party record fuzzily matches to conservative . take the votes record of this row . select the rows whose party record fuzzily matches to labour . take the votes record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; party ; conservative } ; votes } ; hop { filter_eq { all_rows ; party ; labour } ; votes } }, interpretation: select the row whose score record of all rows is minimum . the player record of this row is phil mickelson .
Output: no
Command: eq { hop { argmin { all_rows ; score } ; player } ; phil mickelson }, interpretation: select the row whose score record of all rows is minimum . the player record of this row is phil mickelson .
Output: yes
Command: eq { hop { argmin { all_rows ; score } ; player } ; phil mickelson }, interpretation: for the status records of all rows , most of them fuzzily match to loaned .
Output: no
Command: most_eq { all_rows ; status ; loaned }, interpretation: for the status records of all rows , most of them fuzzily match to loaned .
Output: yes
Command: most_eq { all_rows ; status ; loaned }, interpretation: select the rows whose opponent in the final record fuzzily matches to mark selby . there is only one such row in the table . the championship record of this unqiue row is uk championship .
Output: no
Command: and { only { filter_eq { all_rows ; opponent in the final ; mark selby } } ; eq { hop { filter_eq { all_rows ; opponent in the final ; mark selby } ; championship } ; uk championship } }, interpretation: select the rows whose opponent in the final record fuzzily matches to mark selby . there is only one such row in the table . the championship record of this unqiue row is uk championship .
Output: yes
Command: and { only { filter_eq { all_rows ; opponent in the final ; mark selby } } ; eq { hop { filter_eq { all_rows ; opponent in the final ; mark selby } ; championship } ; uk championship } }, interpretation: for the birth records of all rows , most of them are less than 1800 .
Output: no
Command: most_less { all_rows ; birth ; 1800 }, interpretation: for the birth records of all rows , most of them are less than 1800 .
Output: yes
Command: most_less { all_rows ; birth ; 1800 }, interpretation: for the years records of all rows , most of them fuzzily match to 2001 - 2004 .
Output: no
Command: most_eq { all_rows ; years ; 2001 - 2004 }, interpretation: for the years records of all rows , most of them fuzzily match to 2001 - 2004 .
Output: yes
Command: most_eq { all_rows ; years ; 2001 - 2004 }, interpretation: the average of the rating record of all rows is 4 .
Output: no
Command: round_eq { avg { all_rows ; rating } ; 4 }, interpretation: the average of the rating record of all rows is 4 .
Output: yes
Command: round_eq { avg { all_rows ; rating } ; 4 }, interpretation: select the rows whose album record fuzzily matches to american gangster . take the 1st week sales record of this row . select the rows whose album record fuzzily matches to from nothin ' to somethin' . take the 1st week sales record of this row . the first record is greater than the second record . the 1st week sales record of the first row is 426000 . the 1st week sales record of the second row is 159000 .
Output: no
Command: and { greater { hop { filter_eq { all_rows ; album ; american gangster } ; 1st week sales } ; hop { filter_eq { all_rows ; album ; from nothin ' to somethin' } ; 1st week sales } } ; and { eq { hop { filter_eq { all_rows ; album ; american gangster } ; 1st week sales } ; 426000 } ; eq { hop { filter_eq { all_rows ; album ; from nothin ' to somethin' } ; 1st week sales } ; 159000 } } }, interpretation: select the rows whose album record fuzzily matches to american gangster . take the 1st week sales record of this row . select the rows whose album record fuzzily matches to from nothin ' to somethin' . take the 1st week sales record of this row . the first record is greater than the second record . the 1st week sales record of the first row is 426000 . the 1st week sales record of the second row is 159000 .
Output: yes
Command: and { greater { hop { filter_eq { all_rows ; album ; american gangster } ; 1st week sales } ; hop { filter_eq { all_rows ; album ; from nothin ' to somethin' } ; 1st week sales } } ; and { eq { hop { filter_eq { all_rows ; album ; american gangster } ; 1st week sales } ; 426000 } ; eq { hop { filter_eq { all_rows ; album ; from nothin ' to somethin' } ; 1st week sales } ; 159000 } } }, interpretation: select the row whose matches record of all rows is maximum . the goalkeeper record of this row is chema .
Output: no
Command: eq { hop { argmax { all_rows ; matches } ; goalkeeper } ; chema }, interpretation: select the row whose matches record of all rows is maximum . the goalkeeper record of this row is chema .
Output: yes
Command: eq { hop { argmax { all_rows ; matches } ; goalkeeper } ; chema }, interpretation: select the rows whose visitor record fuzzily matches to pittsburgh . take the date record of this row . select the rows whose visitor record fuzzily matches to boston . take the date record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; visitor ; pittsburgh } ; date } ; hop { filter_eq { all_rows ; visitor ; boston } ; date } }, interpretation: select the rows whose visitor record fuzzily matches to pittsburgh . take the date record of this row . select the rows whose visitor record fuzzily matches to boston . take the date record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; visitor ; pittsburgh } ; date } ; hop { filter_eq { all_rows ; visitor ; boston } ; date } }, interpretation: select the rows whose gpu model record fuzzily matches to hd graphics 4000 . for the l3 cache records of these rows , most of them fuzzily match to 6mb .
Output: no
Command: most_eq { filter_eq { all_rows ; gpu model ; hd graphics 4000 } ; l3 cache ; 6mb }, interpretation: select the rows whose gpu model record fuzzily matches to hd graphics 4000 . for the l3 cache records of these rows , most of them fuzzily match to 6mb .
Output: yes
Command: most_eq { filter_eq { all_rows ; gpu model ; hd graphics 4000 } ; l3 cache ; 6mb }, interpretation: select the rows whose college record fuzzily matches to acadia . there is only one such row in the table . the player record of this unqiue row is karl ortmanns .
Output: no
Command: and { only { filter_eq { all_rows ; college ; acadia } } ; eq { hop { filter_eq { all_rows ; college ; acadia } ; player } ; karl ortmanns } }, interpretation: select the rows whose college record fuzzily matches to acadia . there is only one such row in the table . the player record of this unqiue row is karl ortmanns .
Output: yes
Command: and { only { filter_eq { all_rows ; college ; acadia } } ; eq { hop { filter_eq { all_rows ; college ; acadia } ; player } ; karl ortmanns } }, interpretation: the average of the pick record of all rows is 20.8 .
Output: no
Command: round_eq { avg { all_rows ; pick } ; 20.8 }, interpretation: the average of the pick record of all rows is 20.8 .
Output: yes
Command: round_eq { avg { all_rows ; pick } ; 20.8 }, interpretation: select the rows whose partner record fuzzily matches to igor sijsling . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; partner ; igor sijsling } } ; 2 }, interpretation: select the rows whose partner record fuzzily matches to igor sijsling . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; partner ; igor sijsling } } ; 2 }, interpretation: select the rows whose location record fuzzily matches to fleetcenter . the minimum date record of these rows is fri mar 1 .
Output: no
Command: eq { min { filter_eq { all_rows ; location ; fleetcenter } ; date } ; fri mar 1 }, interpretation: select the rows whose location record fuzzily matches to fleetcenter . the minimum date record of these rows is fri mar 1 .
Output: yes
Command: eq { min { filter_eq { all_rows ; location ; fleetcenter } ; date } ; fri mar 1 }, interpretation: select the rows whose moving parts record fuzzily matches to yes . there is only one such row in the table . the technology record of this unqiue row is flow .
Output: no
Command: and { only { filter_eq { all_rows ; moving parts ; yes } } ; eq { hop { filter_eq { all_rows ; moving parts ; yes } ; technology } ; flow } }, interpretation: select the rows whose moving parts record fuzzily matches to yes . there is only one such row in the table . the technology record of this unqiue row is flow .
Output: yes
Command: and { only { filter_eq { all_rows ; moving parts ; yes } } ; eq { hop { filter_eq { all_rows ; moving parts ; yes } ; technology } ; flow } }, interpretation: select the row whose total record of all rows is maximum . the nation record of this row is south korea .
Output: no
Command: eq { hop { argmax { all_rows ; total } ; nation } ; south korea }, interpretation: select the row whose total record of all rows is maximum . the nation record of this row is south korea .
Output: yes
Command: eq { hop { argmax { all_rows ; total } ; nation } ; south korea }, interpretation: select the rows whose opponent record fuzzily matches to queensland . take the runs record of this row . select the rows whose opponent record fuzzily matches to victoria . take the runs record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; opponent ; queensland } ; runs } ; hop { filter_eq { all_rows ; opponent ; victoria } ; runs } }, interpretation: select the rows whose opponent record fuzzily matches to queensland . take the runs record of this row . select the rows whose opponent record fuzzily matches to victoria . take the runs record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; opponent ; queensland } ; runs } ; hop { filter_eq { all_rows ; opponent ; victoria } ; runs } }, interpretation: select the row whose height ( in ) record of all rows is maximum . the contestant record of this row is happie ntelamo .
Output: no
Command: eq { hop { argmax { all_rows ; height ( in ) } ; contestant } ; happie ntelamo }, interpretation: select the row whose height ( in ) record of all rows is maximum . the contestant record of this row is happie ntelamo .
Output: yes
Command: eq { hop { argmax { all_rows ; height ( in ) } ; contestant } ; happie ntelamo }, interpretation: select the rows whose 1988 record is equal to - . there is only one such row in the table .
Output: no
Command: only { filter_eq { all_rows ; 1988 ; - } }, interpretation: select the rows whose 1988 record is equal to - . there is only one such row in the table .
Output: yes
Command: only { filter_eq { all_rows ; 1988 ; - } }, interpretation: select the rows whose player record fuzzily matches to tom watson . take the total record of this row . select the rows whose player record fuzzily matches to tom weiskopf . take the total record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; player ; tom watson } ; total } ; hop { filter_eq { all_rows ; player ; tom weiskopf } ; total } }, interpretation: select the rows whose player record fuzzily matches to tom watson . take the total record of this row . select the rows whose player record fuzzily matches to tom weiskopf . take the total record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; player ; tom watson } ; total } ; hop { filter_eq { all_rows ; player ; tom weiskopf } ; total } }, interpretation: the average of the matches record of all rows is 51 .
Output: no
Command: round_eq { avg { all_rows ; matches } ; 51 }, interpretation: the average of the matches record of all rows is 51 .
Output: yes
Command: round_eq { avg { all_rows ; matches } ; 51 }, interpretation: select the row whose multi lane record of all rows is maximum . the category wise record of this row is national highways .
Output: no
Command: eq { hop { argmax { all_rows ; multi lane } ; category wise } ; national highways }, interpretation: select the row whose multi lane record of all rows is maximum . the category wise record of this row is national highways .
Output: yes
Command: eq { hop { argmax { all_rows ; multi lane } ; category wise } ; national highways }, interpretation: select the rows whose laps record is less than 5 . there is only one such row in the table . the year record of this unqiue row is 1953 .
Output: no
Command: and { only { filter_less { all_rows ; laps ; 5 } } ; eq { hop { filter_less { all_rows ; laps ; 5 } ; year } ; 1953 } }, interpretation: select the rows whose laps record is less than 5 . there is only one such row in the table . the year record of this unqiue row is 1953 .
Output: yes
Command: and { only { filter_less { all_rows ; laps ; 5 } } ; eq { hop { filter_less { all_rows ; laps ; 5 } ; year } ; 1953 } }, interpretation: for the first issue records of all rows , most of them fuzzily match to august 2008 .
Output: no
Command: most_eq { all_rows ; first issue ; august 2008 }, interpretation: for the first issue records of all rows , most of them fuzzily match to august 2008 .
Output: yes
Command: most_eq { all_rows ; first issue ; august 2008 }, interpretation: select the rows whose from record is greater than or equal to 1930 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_greater_eq { all_rows ; from ; 1930 } } ; 3 }, interpretation: select the rows whose from record is greater than or equal to 1930 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_greater_eq { all_rows ; from ; 1930 } } ; 3 }, interpretation: select the rows whose network record fuzzily matches to the wb . the sum of the viewers in millions record of these rows is 7.62 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; network ; the wb } ; viewers in millions } ; 7.62 }, interpretation: select the rows whose network record fuzzily matches to the wb . the sum of the viewers in millions record of these rows is 7.62 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; network ; the wb } ; viewers in millions } ; 7.62 }, interpretation: select the rows whose equatorial bulge record is less than 100 . select the row whose equatorial diameter record of these rows is 1st maximum . the body record of this row is earth .
Output: no
Command: eq { hop { nth_argmax { filter_less { all_rows ; equatorial bulge ; 100 } ; equatorial diameter ; 1 } ; body } ; earth }, interpretation: select the rows whose equatorial bulge record is less than 100 . select the row whose equatorial diameter record of these rows is 1st maximum . the body record of this row is earth .
Output: yes
Command: eq { hop { nth_argmax { filter_less { all_rows ; equatorial bulge ; 100 } ; equatorial diameter ; 1 } ; body } ; earth }, interpretation: select the rows whose namesake record fuzzily matches to assyrian town where ishtar was worshipped . there is only one such row in the table . the name record of this unqiue row is arbela sulcus .
Output: no
Command: and { only { filter_eq { all_rows ; namesake ; assyrian town where ishtar was worshipped } } ; eq { hop { filter_eq { all_rows ; namesake ; assyrian town where ishtar was worshipped } ; name } ; arbela sulcus } }, interpretation: select the rows whose namesake record fuzzily matches to assyrian town where ishtar was worshipped . there is only one such row in the table . the name record of this unqiue row is arbela sulcus .
Output: yes
Command: and { only { filter_eq { all_rows ; namesake ; assyrian town where ishtar was worshipped } } ; eq { hop { filter_eq { all_rows ; namesake ; assyrian town where ishtar was worshipped } ; name } ; arbela sulcus } }, interpretation: the 4th minimum year record of all rows is 1985 .
Output: no
Command: eq { nth_min { all_rows ; year ; 4 } ; 1985 }, interpretation: the 4th minimum year record of all rows is 1985 .
Output: yes
Command: eq { nth_min { all_rows ; year ; 4 } ; 1985 }, interpretation: select the rows whose location record fuzzily matches to beijing . take the year record of this row . select the rows whose location record fuzzily matches to hiroshima . take the year record of this row . the second record is 4 larger than the first record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; location ; beijing } ; year } ; hop { filter_eq { all_rows ; location ; hiroshima } ; year } } ; -4 }, interpretation: select the rows whose location record fuzzily matches to beijing . take the year record of this row . select the rows whose location record fuzzily matches to hiroshima . take the year record of this row . the second record is 4 larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; location ; beijing } ; year } ; hop { filter_eq { all_rows ; location ; hiroshima } ; year } } ; -4 }, interpretation: the average of the elevation ( m ) record of all rows is 2069.16 .
Output: no
Command: round_eq { avg { all_rows ; elevation ( m ) } ; 2069.16 }, interpretation: the average of the elevation ( m ) record of all rows is 2069.16 .
Output: yes
Command: round_eq { avg { all_rows ; elevation ( m ) } ; 2069.16 }, interpretation: select the rows whose state record fuzzily matches to illinois . among these rows , select the rows whose founded record is equal to 1887 . there is only one such row in the table . the school record of this unqiue row is benedictine university .
Output: no
Command: and { only { filter_eq { filter_eq { all_rows ; state ; illinois } ; founded ; 1887 } } ; eq { hop { filter_eq { filter_eq { all_rows ; state ; illinois } ; founded ; 1887 } ; school } ; benedictine university } }, interpretation: select the rows whose state record fuzzily matches to illinois . among these rows , select the rows whose founded record is equal to 1887 . there is only one such row in the table . the school record of this unqiue row is benedictine university .
Output: yes
Command: and { only { filter_eq { filter_eq { all_rows ; state ; illinois } ; founded ; 1887 } } ; eq { hop { filter_eq { filter_eq { all_rows ; state ; illinois } ; founded ; 1887 } ; school } ; benedictine university } }, interpretation: select the rows whose date record fuzzily matches to december . there is only one such row in the table . the round record of this unqiue row is 20 .
Output: no
Command: and { only { filter_eq { all_rows ; date ; december } } ; eq { hop { filter_eq { all_rows ; date ; december } ; round } ; 20 } }, interpretation: select the rows whose date record fuzzily matches to december . there is only one such row in the table . the round record of this unqiue row is 20 .
Output: yes
Command: and { only { filter_eq { all_rows ; date ; december } } ; eq { hop { filter_eq { all_rows ; date ; december } ; round } ; 20 } }, interpretation: select the rows whose gender record fuzzily matches to m . the maximum votes record of these rows is 11542 .
Output: no
Command: eq { max { filter_eq { all_rows ; gender ; m } ; votes } ; 11542 }, interpretation: select the rows whose gender record fuzzily matches to m . the maximum votes record of these rows is 11542 .
Output: yes
Command: eq { max { filter_eq { all_rows ; gender ; m } ; votes } ; 11542 }, interpretation: select the rows whose attendance record is less than 10000 . there is only one such row in the table . the date record of this unqiue row is march 11 .
Output: no
Command: and { only { filter_less { all_rows ; attendance ; 10000 } } ; eq { hop { filter_less { all_rows ; attendance ; 10000 } ; date } ; march 11 } }, interpretation: select the rows whose attendance record is less than 10000 . there is only one such row in the table . the date record of this unqiue row is march 11 .
Output: yes
Command: and { only { filter_less { all_rows ; attendance ; 10000 } } ; eq { hop { filter_less { all_rows ; attendance ; 10000 } ; date } ; march 11 } }, interpretation: for the general classification records of all rows , most of them fuzzily match to alejandro valverde .
Output: no
Command: most_eq { all_rows ; general classification ; alejandro valverde }, interpretation: for the general classification records of all rows , most of them fuzzily match to alejandro valverde .
Output: yes
Command: most_eq { all_rows ; general classification ; alejandro valverde }, interpretation: select the rows whose name record fuzzily matches to john childress . take the overall record of this row . select the rows whose name record fuzzily matches to claude crabb . take the overall record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; name ; john childress } ; overall } ; hop { filter_eq { all_rows ; name ; claude crabb } ; overall } }, interpretation: select the rows whose name record fuzzily matches to john childress . take the overall record of this row . select the rows whose name record fuzzily matches to claude crabb . take the overall record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; name ; john childress } ; overall } ; hop { filter_eq { all_rows ; name ; claude crabb } ; overall } }, interpretation: select the rows whose vehicle record fuzzily matches to mitsubishi i - miev . take the clean electric grid california ( san francisco ) record of this row . select the rows whose vehicle record fuzzily matches to chevrolet volt . take the clean electric grid california ( san francisco ) record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; vehicle ; mitsubishi i - miev } ; clean electric grid california ( san francisco ) } ; hop { filter_eq { all_rows ; vehicle ; chevrolet volt } ; clean electric grid california ( san francisco ) } }, interpretation: select the rows whose vehicle record fuzzily matches to mitsubishi i - miev . take the clean electric grid california ( san francisco ) record of this row . select the rows whose vehicle record fuzzily matches to chevrolet volt . take the clean electric grid california ( san francisco ) record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; vehicle ; mitsubishi i - miev } ; clean electric grid california ( san francisco ) } ; hop { filter_eq { all_rows ; vehicle ; chevrolet volt } ; clean electric grid california ( san francisco ) } }, interpretation: for the outcome records of all rows , most of them fuzzily match to winner .
Output: no
Command: most_eq { all_rows ; outcome ; winner }, interpretation: select the row whose capacity record of all rows is 2nd minimum . the stadium record of this row is stadio italia .
Output: no
Command: eq { hop { nth_argmin { all_rows ; capacity ; 2 } ; stadium } ; stadio italia }, interpretation: select the row whose capacity record of all rows is 2nd minimum . the stadium record of this row is stadio italia .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; capacity ; 2 } ; stadium } ; stadio italia }, interpretation: select the rows whose team record is arbitrary . the number of such rows is 18 .
Output: no
Command: eq { count { filter_all { all_rows ; team } } ; 18 }, interpretation: select the rows whose team record is arbitrary . the number of such rows is 18 .
Output: yes
Command: eq { count { filter_all { all_rows ; team } } ; 18 }, interpretation: for the result records of all rows , most of them fuzzily match to win .
Output: no
Command: most_eq { all_rows ; result ; win }, interpretation: select the row whose overall pick record of all rows is 3rd minimum . the player record of this row is dennis byrd .
Output: no
Command: eq { hop { nth_argmin { all_rows ; overall pick ; 3 } ; player } ; dennis byrd }, interpretation: select the row whose overall pick record of all rows is 3rd minimum . the player record of this row is dennis byrd .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; overall pick ; 3 } ; player } ; dennis byrd }, interpretation: for the game site records of all rows , most of them fuzzily match to kingdome .
Output: no
Command: most_eq { all_rows ; game site ; kingdome }, interpretation: for the game site records of all rows , most of them fuzzily match to kingdome .
Output: yes
Command: most_eq { all_rows ; game site ; kingdome }, interpretation: select the rows whose owner record fuzzily matches to university . there is only one such row in the table . the frequency record of this unqiue row is fm 101.7 .
Output: no
Command: and { only { filter_eq { all_rows ; owner ; university } } ; eq { hop { filter_eq { all_rows ; owner ; university } ; frequency } ; fm 101.7 } }, interpretation: select the rows whose owner record fuzzily matches to university . there is only one such row in the table . the frequency record of this unqiue row is fm 101.7 .
Output: yes
Command: and { only { filter_eq { all_rows ; owner ; university } } ; eq { hop { filter_eq { all_rows ; owner ; university } ; frequency } ; fm 101.7 } }, interpretation: select the rows whose college record fuzzily matches to detroit mercy . there is only one such row in the table . the player record of this unqiue row is joe kopicki .
Output: no
Command: and { only { filter_eq { all_rows ; college ; detroit mercy } } ; eq { hop { filter_eq { all_rows ; college ; detroit mercy } ; player } ; joe kopicki } }, interpretation: select the rows whose college record fuzzily matches to detroit mercy . there is only one such row in the table . the player record of this unqiue row is joe kopicki .
Output: yes
Command: and { only { filter_eq { all_rows ; college ; detroit mercy } } ; eq { hop { filter_eq { all_rows ; college ; detroit mercy } ; player } ; joe kopicki } }, interpretation: select the rows whose guest record fuzzily matches to garry trudeau . there is only one such row in the table . the episode record of this unqiue row is 806 .
Output: no
Command: and { only { filter_eq { all_rows ; guest ; garry trudeau } } ; eq { hop { filter_eq { all_rows ; guest ; garry trudeau } ; episode } ; 806 } }, interpretation: select the rows whose guest record fuzzily matches to garry trudeau . there is only one such row in the table . the episode record of this unqiue row is 806 .
Output: yes
Command: and { only { filter_eq { all_rows ; guest ; garry trudeau } } ; eq { hop { filter_eq { all_rows ; guest ; garry trudeau } ; episode } ; 806 } }, interpretation: select the rows whose league record is equal to 7 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; league ; 7 } } ; 2 }, interpretation: select the rows whose league record is equal to 7 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; league ; 7 } } ; 2 }, interpretation: select the rows whose opponent record fuzzily matches to sabrina goleš . there is only one such row in the table . the date record of this unqiue row is 23 april 1984 .
Output: no
Command: and { only { filter_eq { all_rows ; opponent ; sabrina goleš } } ; eq { hop { filter_eq { all_rows ; opponent ; sabrina goleš } ; date } ; 23 april 1984 } }, interpretation: select the rows whose opponent record fuzzily matches to sabrina goleš . there is only one such row in the table . the date record of this unqiue row is 23 april 1984 .
Output: yes
Command: and { only { filter_eq { all_rows ; opponent ; sabrina goleš } } ; eq { hop { filter_eq { all_rows ; opponent ; sabrina goleš } ; date } ; 23 april 1984 } }, interpretation: select the rows whose city of license record fuzzily matches to kingston . take the frequency record of this row . select the rows whose city of license record fuzzily matches to pembroke . take the frequency record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; city of license ; kingston } ; frequency } ; hop { filter_eq { all_rows ; city of license ; pembroke } ; frequency } }, interpretation: select the rows whose city of license record fuzzily matches to kingston . take the frequency record of this row . select the rows whose city of license record fuzzily matches to pembroke . take the frequency record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; city of license ; kingston } ; frequency } ; hop { filter_eq { all_rows ; city of license ; pembroke } ; frequency } }, interpretation: select the row whose attendance record of all rows is maximum . the game site record of this row is waldstadion .
Output: no
Command: eq { hop { argmax { all_rows ; attendance } ; game site } ; waldstadion }, interpretation: select the row whose attendance record of all rows is maximum . the game site record of this row is waldstadion .
Output: yes
Command: eq { hop { argmax { all_rows ; attendance } ; game site } ; waldstadion }, interpretation: select the rows whose result record fuzzily matches to won . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; result ; won } } ; 3 }, interpretation: select the rows whose result record fuzzily matches to won . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; result ; won } } ; 3 }, interpretation: for the status records of all rows , all of them fuzzily match to running .
Output: no
Command: all_eq { all_rows ; status ; running }, interpretation: for the status records of all rows , all of them fuzzily match to running .
Output: yes
Command: all_eq { all_rows ; status ; running }, interpretation: select the rows whose platform ( s ) record fuzzily matches to gamecube . there is only one such row in the table . the game record of this unqiue row is resident evil 4 .
Output: no
Command: and { only { filter_eq { all_rows ; platform ( s ) ; gamecube } } ; eq { hop { filter_eq { all_rows ; platform ( s ) ; gamecube } ; game } ; resident evil 4 } }, interpretation: select the rows whose platform ( s ) record fuzzily matches to gamecube . there is only one such row in the table . the game record of this unqiue row is resident evil 4 .
Output: yes
Command: and { only { filter_eq { all_rows ; platform ( s ) ; gamecube } } ; eq { hop { filter_eq { all_rows ; platform ( s ) ; gamecube } ; game } ; resident evil 4 } }, interpretation: the average of the attendance record of all rows is 67503 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 67503 }, interpretation: the average of the attendance record of all rows is 67503 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 67503 }, interpretation: for the national team records of all rows , all of them fuzzily match to brazil .
Output: no
Command: all_eq { all_rows ; national team ; brazil }, interpretation: for the national team records of all rows , all of them fuzzily match to brazil .
Output: yes
Command: all_eq { all_rows ; national team ; brazil }, interpretation: select the row whose us viewers ( million ) record of all rows is maximum . the title record of this row is baby not on board .
Output: no
Command: eq { hop { argmax { all_rows ; us viewers ( million ) } ; title } ; baby not on board }, interpretation: select the row whose us viewers ( million ) record of all rows is maximum . the title record of this row is baby not on board .
Output: yes
Command: eq { hop { argmax { all_rows ; us viewers ( million ) } ; title } ; baby not on board }, interpretation: select the rows whose incumbent record fuzzily matches to sam graves . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to russ carnahan . take the first elected record of this row . the second record is 4 larger than the first record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; incumbent ; sam graves } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; russ carnahan } ; first elected } } ; -4 }, interpretation: select the rows whose incumbent record fuzzily matches to sam graves . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to russ carnahan . take the first elected record of this row . the second record is 4 larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; incumbent ; sam graves } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; russ carnahan } ; first elected } } ; -4 }, interpretation: for the result records of all rows , most of them fuzzily match to win .
Output: no
Command: most_eq { all_rows ; result ; win }, interpretation: select the rows whose opponent record fuzzily matches to chicago blackhawks . take the january record of this row . select the rows whose opponent record fuzzily matches to carolina hurricanes . take the january record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; opponent ; chicago blackhawks } ; january } ; hop { filter_eq { all_rows ; opponent ; carolina hurricanes } ; january } }, interpretation: select the rows whose opponent record fuzzily matches to chicago blackhawks . take the january record of this row . select the rows whose opponent record fuzzily matches to carolina hurricanes . take the january record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; opponent ; chicago blackhawks } ; january } ; hop { filter_eq { all_rows ; opponent ; carolina hurricanes } ; january } }, interpretation: select the row whose - record of all rows is 2nd minimum . the team record of this row is rahal letterman lanigan racing .
Output: no
Command: eq { hop { nth_argmin { all_rows ; - ; 2 } ; team } ; rahal letterman lanigan racing }, interpretation: select the row whose - record of all rows is 2nd minimum . the team record of this row is rahal letterman lanigan racing .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; - ; 2 } ; team } ; rahal letterman lanigan racing }, interpretation: for the headquarters records of all rows , most of them fuzzily match to united states .
Output: no