niryuu/sni-each-converted huggingface dataset:
Command: eq { count { filter_greater { all_rows ; points ; 0 } } ; 2 }, interpretation: the average of the duration record of all rows is 54 .
Output: no
Command: round_eq { avg { all_rows ; duration } ; 54 }, interpretation: the average of the duration record of all rows is 54 .
Output: yes
Command: round_eq { avg { all_rows ; duration } ; 54 }, interpretation: select the rows whose entrant record fuzzily matches to martini racing . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; entrant ; martini racing } } ; 4 }, interpretation: select the rows whose entrant record fuzzily matches to martini racing . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; entrant ; martini racing } } ; 4 }, interpretation: select the rows whose loss record fuzzily matches to reardon . there is only one such row in the table . the date record of this unqiue row is october 18 .
Output: no
Command: and { only { filter_eq { all_rows ; loss ; reardon } } ; eq { hop { filter_eq { all_rows ; loss ; reardon } ; date } ; october 18 } }, interpretation: select the rows whose loss record fuzzily matches to reardon . there is only one such row in the table . the date record of this unqiue row is october 18 .
Output: yes
Command: and { only { filter_eq { all_rows ; loss ; reardon } } ; eq { hop { filter_eq { all_rows ; loss ; reardon } ; date } ; october 18 } }, interpretation: select the row whose viewers ( millions ) record of all rows is maximum . the title record of this row is about face .
Output: no
Command: eq { hop { argmax { all_rows ; viewers ( millions ) } ; title } ; about face }, interpretation: select the row whose viewers ( millions ) record of all rows is maximum . the title record of this row is about face .
Output: yes
Command: eq { hop { argmax { all_rows ; viewers ( millions ) } ; title } ; about face }, interpretation: select the rows whose host record fuzzily matches to allen bestwick . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; host ; allen bestwick } } ; 2 }, interpretation: select the rows whose host record fuzzily matches to allen bestwick . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; host ; allen bestwick } } ; 2 }, interpretation: select the row whose enrollment record of all rows is maximum . the institution record of this row is east carolina university .
Output: no
Command: eq { hop { argmax { all_rows ; enrollment } ; institution } ; east carolina university }, interpretation: select the row whose enrollment record of all rows is maximum . the institution record of this row is east carolina university .
Output: yes
Command: eq { hop { argmax { all_rows ; enrollment } ; institution } ; east carolina university }, interpretation: select the rows whose year record is equal to 2000 . there is only one such row in the table . the name record of this unqiue row is anthony thomas .
Output: no
Command: and { only { filter_eq { all_rows ; year ; 2000 } } ; eq { hop { filter_eq { all_rows ; year ; 2000 } ; name } ; anthony thomas } }, interpretation: select the rows whose year record is equal to 2000 . there is only one such row in the table . the name record of this unqiue row is anthony thomas .
Output: yes
Command: and { only { filter_eq { all_rows ; year ; 2000 } } ; eq { hop { filter_eq { all_rows ; year ; 2000 } ; name } ; anthony thomas } }, interpretation: select the rows whose dates record is less than or equal to 2005 . select the row whose dates record of these rows is 1st minimum . the squadron record of this row is squadron 33 .
Output: no
Command: eq { hop { nth_argmin { filter_less_eq { all_rows ; dates ; 2005 } ; dates ; 1 } ; squadron } ; squadron 33 }, interpretation: select the rows whose dates record is less than or equal to 2005 . select the row whose dates record of these rows is 1st minimum . the squadron record of this row is squadron 33 .
Output: yes
Command: eq { hop { nth_argmin { filter_less_eq { all_rows ; dates ; 2005 } ; dates ; 1 } ; squadron } ; squadron 33 }, interpretation: select the row whose run time record of all rows is maximum . the episode number record of this row is 313 .
Output: no
Command: eq { hop { argmax { all_rows ; run time } ; episode number } ; 313 }, interpretation: select the row whose run time record of all rows is maximum . the episode number record of this row is 313 .
Output: yes
Command: eq { hop { argmax { all_rows ; run time } ; episode number } ; 313 }, interpretation: select the rows whose played record is equal to 114 . for the points records of these rows , most of them are greater than 100 .
Output: no
Command: most_greater { filter_eq { all_rows ; played ; 114 } ; points ; 100 }, interpretation: select the rows whose played record is equal to 114 . for the points records of these rows , most of them are greater than 100 .
Output: yes
Command: most_greater { filter_eq { all_rows ; played ; 114 } ; points ; 100 }, interpretation: select the row whose losses record of all rows is maximum . the team record of this row is liverpool city .
Output: no
Command: eq { hop { argmax { all_rows ; losses } ; team } ; liverpool city }, interpretation: select the row whose losses record of all rows is maximum . the team record of this row is liverpool city .
Output: yes
Command: eq { hop { argmax { all_rows ; losses } ; team } ; liverpool city }, interpretation: select the rows whose high assists record fuzzily matches to kevin garnett . there is only one such row in the table . the date record of this unqiue row is december 21 . the team record of this unqiue row is new york .
Output: no
Command: and { only { filter_eq { all_rows ; high assists ; kevin garnett } } ; and { eq { hop { filter_eq { all_rows ; high assists ; kevin garnett } ; date } ; december 21 } ; eq { hop { filter_eq { all_rows ; high assists ; kevin garnett } ; team } ; new york } } }, interpretation: select the rows whose high assists record fuzzily matches to kevin garnett . there is only one such row in the table . the date record of this unqiue row is december 21 . the team record of this unqiue row is new york .
Output: yes
Command: and { only { filter_eq { all_rows ; high assists ; kevin garnett } } ; and { eq { hop { filter_eq { all_rows ; high assists ; kevin garnett } ; date } ; december 21 } ; eq { hop { filter_eq { all_rows ; high assists ; kevin garnett } ; team } ; new york } } }, interpretation: select the rows whose 2nd evening record fuzzily matches to n / a . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; 2nd evening ; n / a } } ; 3 }, interpretation: select the rows whose 2nd evening record fuzzily matches to n / a . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; 2nd evening ; n / a } } ; 3 }, interpretation: select the rows whose date record fuzzily matches to november . for the result records of these rows , most of them fuzzily match to w .
Output: no
Command: most_eq { filter_eq { all_rows ; date ; november } ; result ; w }, interpretation: select the rows whose date record fuzzily matches to november . for the result records of these rows , most of them fuzzily match to w .
Output: yes
Command: most_eq { filter_eq { all_rows ; date ; november } ; result ; w }, interpretation: select the rows whose venue record fuzzily matches to san francisco . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; venue ; san francisco } } ; 2 }, interpretation: select the rows whose venue record fuzzily matches to san francisco . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; venue ; san francisco } } ; 2 }, interpretation: select the rows whose result record fuzzily matches to win . the number of such rows is 6 .
Output: no
Command: eq { count { filter_eq { all_rows ; result ; win } } ; 6 }, interpretation: select the rows whose result record fuzzily matches to win . the number of such rows is 6 .
Output: yes
Command: eq { count { filter_eq { all_rows ; result ; win } } ; 6 }, interpretation: select the rows whose manner of departure record fuzzily matches to mutual consent . there is only one such row in the table . the outgoing manager record of this unqiue row is lee sinnott .
Output: no
Command: and { only { filter_eq { all_rows ; manner of departure ; mutual consent } } ; eq { hop { filter_eq { all_rows ; manner of departure ; mutual consent } ; outgoing manager } ; lee sinnott } }, interpretation: select the rows whose manner of departure record fuzzily matches to mutual consent . there is only one such row in the table . the outgoing manager record of this unqiue row is lee sinnott .
Output: yes
Command: and { only { filter_eq { all_rows ; manner of departure ; mutual consent } } ; eq { hop { filter_eq { all_rows ; manner of departure ; mutual consent } ; outgoing manager } ; lee sinnott } }, interpretation: select the rows whose region record fuzzily matches to kansai . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; region ; kansai } } ; 2 }, interpretation: select the rows whose region record fuzzily matches to kansai . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; region ; kansai } } ; 2 }, interpretation: select the rows whose mlb draft record fuzzily matches to pirates . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; mlb draft ; pirates } } ; 2 }, interpretation: select the rows whose mlb draft record fuzzily matches to pirates . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; mlb draft ; pirates } } ; 2 }, interpretation: select the rows whose years record fuzzily matches to 1 - 8 . among these rows , select the rows whose authority record fuzzily matches to integrated . there is only one such row in the table . the name record of this unqiue row is st teresa 's school .
Output: no
Command: and { only { filter_eq { filter_eq { all_rows ; years ; 1 - 8 } ; authority ; integrated } } ; eq { hop { filter_eq { filter_eq { all_rows ; years ; 1 - 8 } ; authority ; integrated } ; name } ; st teresa 's school } }, interpretation: select the rows whose years record fuzzily matches to 1 - 8 . among these rows , select the rows whose authority record fuzzily matches to integrated . there is only one such row in the table . the name record of this unqiue row is st teresa 's school .
Output: yes
Command: and { only { filter_eq { filter_eq { all_rows ; years ; 1 - 8 } ; authority ; integrated } } ; eq { hop { filter_eq { filter_eq { all_rows ; years ; 1 - 8 } ; authority ; integrated } ; name } ; st teresa 's school } }, interpretation: select the row whose time record of all rows is 5th minimum . the athlete record of this row is craig virgin .
Output: no
Command: eq { hop { nth_argmin { all_rows ; time ; 5 } ; athlete } ; craig virgin }, interpretation: select the row whose time record of all rows is 5th minimum . the athlete record of this row is craig virgin .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; time ; 5 } ; athlete } ; craig virgin }, interpretation: select the row whose bronze record of all rows is maximum . the nation record of this row is spain .
Output: no
Command: eq { hop { argmax { all_rows ; bronze } ; nation } ; spain }, interpretation: select the row whose bronze record of all rows is maximum . the nation record of this row is spain .
Output: yes
Command: eq { hop { argmax { all_rows ; bronze } ; nation } ; spain }, interpretation: for the points records of all rows , most of them are equal to 0 .
Output: no
Command: most_eq { all_rows ; points ; 0 }, interpretation: for the points records of all rows , most of them are equal to 0 .
Output: yes
Command: most_eq { all_rows ; points ; 0 }, interpretation: select the row whose attendance record of all rows is 2nd maximum . the site record of this row is ben hill griffin stadium gainesville , fl .
Output: no
Command: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; site } ; ben hill griffin stadium gainesville , fl }, interpretation: select the row whose attendance record of all rows is 2nd maximum . the site record of this row is ben hill griffin stadium gainesville , fl .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; site } ; ben hill griffin stadium gainesville , fl }, interpretation: for the party records of all rows , most of them fuzzily match to republican .
Output: no
Command: most_eq { all_rows ; party ; republican }, interpretation: select the rows whose co - driver record fuzzily matches to leo kinnunen . for the pos records of these rows , most of them fuzzily match to ret .
Output: no
Command: most_eq { filter_eq { all_rows ; co - driver ; leo kinnunen } ; pos ; ret }, interpretation: select the rows whose co - driver record fuzzily matches to leo kinnunen . for the pos records of these rows , most of them fuzzily match to ret .
Output: yes
Command: most_eq { filter_eq { all_rows ; co - driver ; leo kinnunen } ; pos ; ret }, interpretation: for the result records of all rows , most of them fuzzily match to re - elected .
Output: no
Command: most_eq { all_rows ; result ; re - elected }, interpretation: select the rows whose college record fuzzily matches to new mexico . there is only one such row in the table . the player record of this unqiue row is brian ramsay .
Output: no
Command: and { only { filter_eq { all_rows ; college ; new mexico } } ; eq { hop { filter_eq { all_rows ; college ; new mexico } ; player } ; brian ramsay } }, interpretation: select the rows whose college record fuzzily matches to new mexico . there is only one such row in the table . the player record of this unqiue row is brian ramsay .
Output: yes
Command: and { only { filter_eq { all_rows ; college ; new mexico } } ; eq { hop { filter_eq { all_rows ; college ; new mexico } ; player } ; brian ramsay } }, interpretation: select the rows whose men 's singles record fuzzily matches to tan joe hok . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; men 's singles ; tan joe hok } } ; 2 }, interpretation: select the rows whose men 's singles record fuzzily matches to tan joe hok . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; men 's singles ; tan joe hok } } ; 2 }, interpretation: select the rows whose school colors record fuzzily matches to orange & black . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; school colors ; orange & black } } ; 2 }, interpretation: select the rows whose school colors record fuzzily matches to orange & black . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; school colors ; orange & black } } ; 2 }, interpretation: select the rows whose time record fuzzily matches to 5:00 . the number of such rows is 8 .
Output: no
Command: eq { count { filter_eq { all_rows ; time ; 5:00 } } ; 8 }, interpretation: select the rows whose time record fuzzily matches to 5:00 . the number of such rows is 8 .
Output: yes
Command: eq { count { filter_eq { all_rows ; time ; 5:00 } } ; 8 }, interpretation: select the row whose total record of all rows is maximum . the nation record of this row is germany .
Output: no
Command: eq { hop { argmax { all_rows ; total } ; nation } ; germany }, interpretation: select the row whose total record of all rows is maximum . the nation record of this row is germany .
Output: yes
Command: eq { hop { argmax { all_rows ; total } ; nation } ; germany }, interpretation: the minimum date record of all rows is september 4 , 1994 .
Output: no
Command: eq { min { all_rows ; date } ; september 4 , 1994 }, interpretation: the minimum date record of all rows is september 4 , 1994 .
Output: yes
Command: eq { min { all_rows ; date } ; september 4 , 1994 }, interpretation: select the row whose date record of all rows is 2nd maximum . the tournament record of this row is moscow .
Output: no
Command: eq { hop { nth_argmax { all_rows ; date ; 2 } ; tournament } ; moscow }, interpretation: select the row whose date record of all rows is 2nd maximum . the tournament record of this row is moscow .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; date ; 2 } ; tournament } ; moscow }, interpretation: select the rows whose round record is equal to 7 . among these rows , select the rows whose college record fuzzily matches to oregon . there is only one such row in the table . the name record of this unqiue row is steve smith .
Output: no
Command: and { only { filter_eq { filter_eq { all_rows ; round ; 7 } ; college ; oregon } } ; eq { hop { filter_eq { filter_eq { all_rows ; round ; 7 } ; college ; oregon } ; name } ; steve smith } }, interpretation: select the rows whose round record is equal to 7 . among these rows , select the rows whose college record fuzzily matches to oregon . there is only one such row in the table . the name record of this unqiue row is steve smith .
Output: yes
Command: and { only { filter_eq { filter_eq { all_rows ; round ; 7 } ; college ; oregon } } ; eq { hop { filter_eq { filter_eq { all_rows ; round ; 7 } ; college ; oregon } ; name } ; steve smith } }, interpretation: select the rows whose name record fuzzily matches to kara lynn joyce . take the time record of this row . select the rows whose name record fuzzily matches to aliaksandra herasimenia . take the time record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; name ; kara lynn joyce } ; time } ; hop { filter_eq { all_rows ; name ; aliaksandra herasimenia } ; time } }, interpretation: select the rows whose name record fuzzily matches to kara lynn joyce . take the time record of this row . select the rows whose name record fuzzily matches to aliaksandra herasimenia . take the time record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; name ; kara lynn joyce } ; time } ; hop { filter_eq { all_rows ; name ; aliaksandra herasimenia } ; time } }, interpretation: select the rows whose competition record fuzzily matches to uefa euro 2004 qualifying . there is only one such row in the table . the date record of this unqiue row is 2003 - 02 - 04 .
Output: no
Command: and { only { filter_eq { all_rows ; competition ; uefa euro 2004 qualifying } } ; eq { hop { filter_eq { all_rows ; competition ; uefa euro 2004 qualifying } ; date } ; 2003 - 02 - 04 } }, interpretation: select the rows whose competition record fuzzily matches to uefa euro 2004 qualifying . there is only one such row in the table . the date record of this unqiue row is 2003 - 02 - 04 .
Output: yes
Command: and { only { filter_eq { all_rows ; competition ; uefa euro 2004 qualifying } } ; eq { hop { filter_eq { all_rows ; competition ; uefa euro 2004 qualifying } ; date } ; 2003 - 02 - 04 } }, interpretation: select the rows whose wins record is equal to 10 . there is only one such row in the table . the club record of this unqiue row is east bengal club .
Output: no
Command: and { only { filter_eq { all_rows ; wins ; 10 } } ; eq { hop { filter_eq { all_rows ; wins ; 10 } ; club } ; east bengal club } }, interpretation: select the rows whose wins record is equal to 10 . there is only one such row in the table . the club record of this unqiue row is east bengal club .
Output: yes
Command: and { only { filter_eq { all_rows ; wins ; 10 } } ; eq { hop { filter_eq { all_rows ; wins ; 10 } ; club } ; east bengal club } }, interpretation: select the rows whose laps record is equal to 200 . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; laps ; 200 } } ; 4 }, interpretation: select the rows whose laps record is equal to 200 . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; laps ; 200 } } ; 4 }, interpretation: select the rows whose general classification record fuzzily matches to nick nuyens . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; general classification ; nick nuyens } } ; 5 }, interpretation: select the rows whose general classification record fuzzily matches to nick nuyens . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { all_rows ; general classification ; nick nuyens } } ; 5 }, interpretation: for the competition records of all rows , most of them fuzzily match to 2014 fifa world cup qualification .
Output: no
Command: most_eq { all_rows ; competition ; 2014 fifa world cup qualification }, interpretation: for the competition records of all rows , most of them fuzzily match to 2014 fifa world cup qualification .
Output: yes
Command: most_eq { all_rows ; competition ; 2014 fifa world cup qualification }, interpretation: select the rows whose tournament record fuzzily matches to buenos aires . take the date record of this row . select the rows whose tournament record fuzzily matches to panama city . 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 ; tournament ; buenos aires } ; date } ; hop { filter_eq { all_rows ; tournament ; panama city } ; date } }, interpretation: select the rows whose tournament record fuzzily matches to buenos aires . take the date record of this row . select the rows whose tournament record fuzzily matches to panama city . 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 ; tournament ; buenos aires } ; date } ; hop { filter_eq { all_rows ; tournament ; panama city } ; date } }, interpretation: the average of the launch record of all rows is 2001-2002 .
Output: no
Command: round_eq { avg { all_rows ; launch } ; 2001-2002 }, interpretation: the average of the launch record of all rows is 2001-2002 .
Output: yes
Command: round_eq { avg { all_rows ; launch } ; 2001-2002 }, interpretation: for the score records of all rows , most of them fuzzily match to l .
Output: no
Command: most_eq { all_rows ; score ; l }, interpretation: for the date of vacancy records of all rows , most of them fuzzily match to 2010 .
Output: no
Command: most_eq { all_rows ; date of vacancy ; 2010 }, interpretation: for the date of vacancy records of all rows , most of them fuzzily match to 2010 .
Output: yes
Command: most_eq { all_rows ; date of vacancy ; 2010 }, interpretation: for the type records of all rows , all of them fuzzily match to private .
Output: no
Command: all_eq { all_rows ; type ; private }, interpretation: select the rows whose total record is less than 13 . the sum of the silver record of these rows is 2 .
Output: no
Command: round_eq { sum { filter_less { all_rows ; total ; 13 } ; silver } ; 2 }, interpretation: select the rows whose total record is less than 13 . the sum of the silver record of these rows is 2 .
Output: yes
Command: round_eq { sum { filter_less { all_rows ; total ; 13 } ; silver } ; 2 }, interpretation: the minimum finish position record of all rows is 36th .
Output: no
Command: eq { min { all_rows ; finish position } ; 36th }, interpretation: the minimum finish position record of all rows is 36th .
Output: yes
Command: eq { min { all_rows ; finish position } ; 36th }, interpretation: select the rows whose date record fuzzily matches to april 18 . take the score record of this row . select the rows whose date record fuzzily matches to april 21 . take the score record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; date ; april 18 } ; score } ; hop { filter_eq { all_rows ; date ; april 21 } ; score } }, interpretation: select the rows whose date record fuzzily matches to april 18 . take the score record of this row . select the rows whose date record fuzzily matches to april 21 . take the score record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; date ; april 18 } ; score } ; hop { filter_eq { all_rows ; date ; april 21 } ; score } }, interpretation: the average of the away team score record of all rows is 14.2 .
Output: no
Command: round_eq { avg { all_rows ; away team score } ; 14.2 }, interpretation: the average of the away team score record of all rows is 14.2 .
Output: yes
Command: round_eq { avg { all_rows ; away team score } ; 14.2 }, interpretation: select the rows whose station record fuzzily matches to kami - mio . take the distance ( km ) record of this row . select the rows whose station record fuzzily matches to funao . take the distance ( km ) record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; station ; kami - mio } ; distance ( km ) } ; hop { filter_eq { all_rows ; station ; funao } ; distance ( km ) } }, interpretation: select the rows whose station record fuzzily matches to kami - mio . take the distance ( km ) record of this row . select the rows whose station record fuzzily matches to funao . take the distance ( km ) record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; station ; kami - mio } ; distance ( km ) } ; hop { filter_eq { all_rows ; station ; funao } ; distance ( km ) } }, interpretation: select the rows whose gold record is equal to 7 . there is only one such row in the table . the nation record of this unqiue row is brazil ( bra ) .
Output: no
Command: and { only { filter_eq { all_rows ; gold ; 7 } } ; eq { hop { filter_eq { all_rows ; gold ; 7 } ; nation } ; brazil ( bra ) } }, interpretation: select the rows whose gold record is equal to 7 . there is only one such row in the table . the nation record of this unqiue row is brazil ( bra ) .
Output: yes
Command: and { only { filter_eq { all_rows ; gold ; 7 } } ; eq { hop { filter_eq { all_rows ; gold ; 7 } ; nation } ; brazil ( bra ) } }, interpretation: select the rows whose chassis record fuzzily matches to connew pc1 . the number of such rows is 1 .
Output: no
Command: eq { count { filter_eq { all_rows ; chassis ; connew pc1 } } ; 1 }, interpretation: select the rows whose chassis record fuzzily matches to connew pc1 . the number of such rows is 1 .
Output: yes
Command: eq { count { filter_eq { all_rows ; chassis ; connew pc1 } } ; 1 }, interpretation: select the rows whose club record fuzzily matches to biu chun rangers . take the position in 2012 - 13 record of this row . select the rows whose club record fuzzily matches to citizen . take the position in 2012 - 13 record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; club ; biu chun rangers } ; position in 2012 - 13 } ; hop { filter_eq { all_rows ; club ; citizen } ; position in 2012 - 13 } }, interpretation: select the rows whose club record fuzzily matches to biu chun rangers . take the position in 2012 - 13 record of this row . select the rows whose club record fuzzily matches to citizen . take the position in 2012 - 13 record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; club ; biu chun rangers } ; position in 2012 - 13 } ; hop { filter_eq { all_rows ; club ; citizen } ; position in 2012 - 13 } }, interpretation: for the high rebounds records of all rows , most of them fuzzily match to chris bosh .
Output: no
Command: most_eq { all_rows ; high rebounds ; chris bosh }, interpretation: for the high rebounds records of all rows , most of them fuzzily match to chris bosh .
Output: yes
Command: most_eq { all_rows ; high rebounds ; chris bosh }, interpretation: select the rows whose polyunsaturated fat record fuzzily matches to 3 % . there is only one such row in the table . the total fat record of this unqiue row is 94 g .
Output: no
Command: and { only { filter_eq { all_rows ; polyunsaturated fat ; 3 % } } ; eq { hop { filter_eq { all_rows ; polyunsaturated fat ; 3 % } ; total fat } ; 94 g } }, interpretation: select the rows whose polyunsaturated fat record fuzzily matches to 3 % . there is only one such row in the table . the total fat record of this unqiue row is 94 g .
Output: yes
Command: and { only { filter_eq { all_rows ; polyunsaturated fat ; 3 % } } ; eq { hop { filter_eq { all_rows ; polyunsaturated fat ; 3 % } ; total fat } ; 94 g } }, interpretation: select the rows whose wins record is equal to 1 . the number of such rows is 1 .
Output: no
Command: eq { count { filter_eq { all_rows ; wins ; 1 } } ; 1 }, interpretation: select the rows whose wins record is equal to 1 . the number of such rows is 1 .
Output: yes
Command: eq { count { filter_eq { all_rows ; wins ; 1 } } ; 1 }, interpretation: for the w / l records of all rows , most of them fuzzily match to loss .
Output: no
Command: most_eq { all_rows ; w / l ; loss }, interpretation: for the w / l records of all rows , most of them fuzzily match to loss .
Output: yes
Command: most_eq { all_rows ; w / l ; loss }, interpretation: select the row whose enrollment record of all rows is maximum . the high school record of this row is mount tahoma .
Output: no
Command: eq { hop { argmax { all_rows ; enrollment } ; high school } ; mount tahoma }, interpretation: select the row whose enrollment record of all rows is maximum . the high school record of this row is mount tahoma .
Output: yes
Command: eq { hop { argmax { all_rows ; enrollment } ; high school } ; mount tahoma }, interpretation: select the rows whose player record fuzzily matches to dwight lewis . take the round record of this row . select the rows whose player record fuzzily matches to doug hogan . take the round record of this row . the first record is less than the second record . the round record of the first row is 10 . the round record of the second row is 16 .
Output: no
Command: and { less { hop { filter_eq { all_rows ; player ; dwight lewis } ; round } ; hop { filter_eq { all_rows ; player ; doug hogan } ; round } } ; and { eq { hop { filter_eq { all_rows ; player ; dwight lewis } ; round } ; 10 } ; eq { hop { filter_eq { all_rows ; player ; doug hogan } ; round } ; 16 } } }, interpretation: select the rows whose player record fuzzily matches to dwight lewis . take the round record of this row . select the rows whose player record fuzzily matches to doug hogan . take the round record of this row . the first record is less than the second record . the round record of the first row is 10 . the round record of the second row is 16 .
Output: yes
Command: and { less { hop { filter_eq { all_rows ; player ; dwight lewis } ; round } ; hop { filter_eq { all_rows ; player ; doug hogan } ; round } } ; and { eq { hop { filter_eq { all_rows ; player ; dwight lewis } ; round } ; 10 } ; eq { hop { filter_eq { all_rows ; player ; doug hogan } ; round } ; 16 } } }, interpretation: select the rows whose authority record fuzzily matches to state integrated . there is only one such row in the table . the name record of this unqiue row is st joseph 's catholic school .
Output: no
Command: and { only { filter_eq { all_rows ; authority ; state integrated } } ; eq { hop { filter_eq { all_rows ; authority ; state integrated } ; name } ; st joseph 's catholic school } }, interpretation: select the rows whose authority record fuzzily matches to state integrated . there is only one such row in the table . the name record of this unqiue row is st joseph 's catholic school .
Output: yes
Command: and { only { filter_eq { all_rows ; authority ; state integrated } } ; eq { hop { filter_eq { all_rows ; authority ; state integrated } ; name } ; st joseph 's catholic school } }, interpretation: select the rows whose rank record is less than or equal to 2 . the sum of the wins record of these rows is 35 .
Output: no
Command: round_eq { sum { filter_less_eq { all_rows ; rank ; 2 } ; wins } ; 35 }, interpretation: select the rows whose rank record is less than or equal to 2 . the sum of the wins record of these rows is 35 .
Output: yes
Command: round_eq { sum { filter_less_eq { all_rows ; rank ; 2 } ; wins } ; 35 }, interpretation: select the rows whose avg attend record is less than 4000 . there is only one such row in the table . the indoor year record of this unqiue row is 1983 - 1984 .
Output: no
Command: and { only { filter_less { all_rows ; avg attend ; 4000 } } ; eq { hop { filter_less { all_rows ; avg attend ; 4000 } ; indoor year } ; 1983 - 1984 } }, interpretation: select the rows whose avg attend record is less than 4000 . there is only one such row in the table . the indoor year record of this unqiue row is 1983 - 1984 .
Output: yes
Command: and { only { filter_less { all_rows ; avg attend ; 4000 } } ; eq { hop { filter_less { all_rows ; avg attend ; 4000 } ; indoor year } ; 1983 - 1984 } }, interpretation: select the rows whose status record fuzzily matches to village . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; status ; village } } ; 4 }, interpretation: select the rows whose status record fuzzily matches to village . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; status ; village } } ; 4 }, interpretation: select the rows whose opponent record fuzzily matches to philadelphia eagles . there is only one such row in the table . the week record of this unqiue row is 1 .
Output: no
Command: and { only { filter_eq { all_rows ; opponent ; philadelphia eagles } } ; eq { hop { filter_eq { all_rows ; opponent ; philadelphia eagles } ; week } ; 1 } }, interpretation: select the rows whose opponent record fuzzily matches to philadelphia eagles . there is only one such row in the table . the week record of this unqiue row is 1 .
Output: yes
Command: and { only { filter_eq { all_rows ; opponent ; philadelphia eagles } } ; eq { hop { filter_eq { all_rows ; opponent ; philadelphia eagles } ; week } ; 1 } }, interpretation: select the rows whose performer 2 record is arbitrary . the number of such rows is 6 .
Output: no
Command: eq { count { filter_all { all_rows ; performer 2 } } ; 6 }, interpretation: select the rows whose performer 2 record is arbitrary . the number of such rows is 6 .
Output: yes
Command: eq { count { filter_all { all_rows ; performer 2 } } ; 6 }, interpretation: select the rows whose l record is equal to 0 . there is only one such row in the table . the skip ( club ) record of this unqiue row is brad gushue ( bally haly ) .
Output: no
Command: and { only { filter_eq { all_rows ; l ; 0 } } ; eq { hop { filter_eq { all_rows ; l ; 0 } ; skip ( club ) } ; brad gushue ( bally haly ) } }, interpretation: select the rows whose l record is equal to 0 . there is only one such row in the table . the skip ( club ) record of this unqiue row is brad gushue ( bally haly ) .
Output: yes
Command: and { only { filter_eq { all_rows ; l ; 0 } } ; eq { hop { filter_eq { all_rows ; l ; 0 } ; skip ( club ) } ; brad gushue ( bally haly ) } }, interpretation: select the rows whose location record fuzzily matches to marquette , michigan . there is only one such row in the table . the institution record of this unqiue row is northern michigan university .
Output: no
Command: and { only { filter_eq { all_rows ; location ; marquette , michigan } } ; eq { hop { filter_eq { all_rows ; location ; marquette , michigan } ; institution } ; northern michigan university } }, interpretation: select the rows whose location record fuzzily matches to marquette , michigan . there is only one such row in the table . the institution record of this unqiue row is northern michigan university .
Output: yes
Command: and { only { filter_eq { all_rows ; location ; marquette , michigan } } ; eq { hop { filter_eq { all_rows ; location ; marquette , michigan } ; institution } ; northern michigan university } }, interpretation: select the row whose start record of all rows is minimum . the year record of this row is 1954 .
Output: no
Command: eq { hop { argmin { all_rows ; start } ; year } ; 1954 }, interpretation: select the row whose start record of all rows is minimum . the year record of this row is 1954 .
Output: yes
Command: eq { hop { argmin { all_rows ; start } ; year } ; 1954 }, interpretation: select the rows whose location record fuzzily matches to st pete times forum . among these rows , select the rows whose attendance record is greater than 16000 . the number of such rows is 5 .
Output: no
Command: eq { count { filter_greater { filter_eq { all_rows ; location ; st pete times forum } ; attendance ; 16000 } } ; 5 }, interpretation: select the rows whose location record fuzzily matches to st pete times forum . among these rows , select the rows whose attendance record is greater than 16000 . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_greater { filter_eq { all_rows ; location ; st pete times forum } ; attendance ; 16000 } } ; 5 }, interpretation: select the rows whose high assists record fuzzily matches to steve blake . among these rows , select the rows whose score record fuzzily matches to w . there is only one such row in the table . the date record of this unqiue row is april 21 .
Output: no
Command: and { only { filter_eq { filter_eq { all_rows ; high assists ; steve blake } ; score ; w } } ; eq { hop { filter_eq { filter_eq { all_rows ; high assists ; steve blake } ; score ; w } ; date } ; april 21 } }, interpretation: select the rows whose high assists record fuzzily matches to steve blake . among these rows , select the rows whose score record fuzzily matches to w . there is only one such row in the table . the date record of this unqiue row is april 21 .
Output: yes
Command: and { only { filter_eq { filter_eq { all_rows ; high assists ; steve blake } ; score ; w } } ; eq { hop { filter_eq { filter_eq { all_rows ; high assists ; steve blake } ; score ; w } ; date } ; april 21 } }, interpretation: the average of the total record of all rows is 3.75 .
Output: no
Command: round_eq { avg { all_rows ; total } ; 3.75 }, interpretation: the average of the total record of all rows is 3.75 .
Output: yes
Command: round_eq { avg { all_rows ; total } ; 3.75 }, interpretation: select the rows whose position record fuzzily matches to cornerback . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; position ; cornerback } } ; 2 }, interpretation: select the rows whose position record fuzzily matches to cornerback . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; position ; cornerback } } ; 2 }, interpretation: select the row whose score record of all rows is 2nd minimum . the player record of this row is steve jones .
Output: no
Command: eq { hop { nth_argmin { all_rows ; score ; 2 } ; player } ; steve jones }, interpretation: select the row whose score record of all rows is 2nd minimum . the player record of this row is steve jones .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; score ; 2 } ; player } ; steve jones }, interpretation: select the row whose attendance record of all rows is 2nd maximum . the home team record of this row is milton keynes dons .
Output: no
Command: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; home team } ; milton keynes dons }, interpretation: select the row whose attendance record of all rows is 2nd maximum . the home team record of this row is milton keynes dons .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; home team } ; milton keynes dons }, interpretation: select the rows whose position record fuzzily matches to running back . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; position ; running back } } ; 3 }, interpretation: select the rows whose position record fuzzily matches to running back . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; position ; running back } } ; 3 }, interpretation: for the championships records of all rows , most of them are greater than or equal to 1 .
Output: no
Command: most_greater_eq { all_rows ; championships ; 1 }, interpretation: for the championships records of all rows , most of them are greater than or equal to 1 .
Output: yes
Command: most_greater_eq { all_rows ; championships ; 1 }, interpretation: select the rows whose name record fuzzily matches to over the deel . take the age record of this row . select the rows whose name record fuzzily matches to captain dibble . take the age record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; name ; over the deel } ; age } ; hop { filter_eq { all_rows ; name ; captain dibble } ; age } }, interpretation: select the rows whose name record fuzzily matches to over the deel . take the age record of this row . select the rows whose name record fuzzily matches to captain dibble . take the age record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; name ; over the deel } ; age } ; hop { filter_eq { all_rows ; name ; captain dibble } ; age } }, interpretation: select the rows whose location record fuzzily matches to sheffield , england . among these rows , select the rows whose type record fuzzily matches to club trophy . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; location ; sheffield , england } ; type ; club trophy } } ; 2 }, interpretation: select the rows whose location record fuzzily matches to sheffield , england . among these rows , select the rows whose type record fuzzily matches to club trophy . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; location ; sheffield , england } ; type ; club trophy } } ; 2 }, interpretation: select the row whose joined record of all rows is 3rd maximum . the school record of this row is south bend clay .
Output: no
Command: eq { hop { nth_argmax { all_rows ; joined ; 3 } ; school } ; south bend clay }, interpretation: select the row whose joined record of all rows is 3rd maximum . the school record of this row is south bend clay .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; joined ; 3 } ; school } ; south bend clay }, interpretation: select the row whose points record of all rows is maximum . the club record of this row is ud las palmas .
Output: no
Command: eq { hop { argmax { all_rows ; points } ; club } ; ud las palmas }, interpretation: select the row whose points record of all rows is maximum . the club record of this row is ud las palmas .
Output: yes
Command: eq { hop { argmax { all_rows ; points } ; club } ; ud las palmas }, interpretation: select the rows whose opponent record fuzzily matches to detroit red wings . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; opponent ; detroit red wings } } ; 3 }, interpretation: select the rows whose opponent record fuzzily matches to detroit red wings . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; opponent ; detroit red wings } } ; 3 }, interpretation: select the rows whose race title record is arbitrary . the number of such rows is 5 .
Output: no
Command: eq { count { filter_all { all_rows ; race title } } ; 5 }, interpretation: select the rows whose race title record is arbitrary . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_all { all_rows ; race title } } ; 5 }, interpretation: the average of the total record of all rows is 3 .
Output: no
Command: round_eq { avg { all_rows ; total } ; 3 }, interpretation: the average of the total record of all rows is 3 .
Output: yes
Command: round_eq { avg { all_rows ; total } ; 3 }, interpretation: select the rows whose site / stadium record fuzzily matches to goodwin field . there is only one such row in the table . the date record of this unqiue row is may 25 . the opponent record of this unqiue row is cal state fullerton .
Output: no
Command: and { only { filter_eq { all_rows ; site / stadium ; goodwin field } } ; and { eq { hop { filter_eq { all_rows ; site / stadium ; goodwin field } ; date } ; may 25 } ; eq { hop { filter_eq { all_rows ; site / stadium ; goodwin field } ; opponent } ; cal state fullerton } } }, interpretation: select the rows whose site / stadium record fuzzily matches to goodwin field . there is only one such row in the table . the date record of this unqiue row is may 25 . the opponent record of this unqiue row is cal state fullerton .
Output: yes
Command: and { only { filter_eq { all_rows ; site / stadium ; goodwin field } } ; and { eq { hop { filter_eq { all_rows ; site / stadium ; goodwin field } ; date } ; may 25 } ; eq { hop { filter_eq { all_rows ; site / stadium ; goodwin field } ; opponent } ; cal state fullerton } } }, interpretation: select the rows whose model record fuzzily matches to nd4 . take the top speed ( in operation ) （ km / h ） record of this row . select the rows whose model record fuzzily matches to nd5 . take the top speed ( in operation ) （ km / h ） record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; model ; nd4 } ; top speed ( in operation ) （ km / h ） } ; hop { filter_eq { all_rows ; model ; nd5 } ; top speed ( in operation ) （ km / h ） } }, interpretation: select the rows whose model record fuzzily matches to nd4 . take the top speed ( in operation ) （ km / h ） record of this row . select the rows whose model record fuzzily matches to nd5 . take the top speed ( in operation ) （ km / h ） record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; model ; nd4 } ; top speed ( in operation ) （ km / h ） } ; hop { filter_eq { all_rows ; model ; nd5 } ; top speed ( in operation ) （ km / h ） } }, interpretation: select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose to par record is equal to -1 . there is only one such row in the table . the player record of this unqiue row is jack nicklaus .
Output: no
Command: and { only { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; -1 } } ; eq { hop { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; -1 } ; player } ; jack nicklaus } }, interpretation: select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose to par record is equal to -1 . there is only one such row in the table . the player record of this unqiue row is jack nicklaus .
Output: yes
Command: and { only { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; -1 } } ; eq { hop { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; -1 } ; player } ; jack nicklaus } }, interpretation: select the row whose time record of all rows is maximum . the game record of this row is 1 .
Output: no
Command: eq { hop { argmax { all_rows ; time } ; game } ; 1 }, interpretation: select the row whose time record of all rows is maximum . the game record of this row is 1 .
Output: yes
Command: eq { hop { argmax { all_rows ; time } ; game } ; 1 }, interpretation: for the format ( s ) records of all rows , most of them fuzzily match to cd .
Output: no
Command: most_eq { all_rows ; format ( s ) ; cd }, interpretation: for the format ( s ) records of all rows , most of them fuzzily match to cd .
Output: yes
Command: most_eq { all_rows ; format ( s ) ; cd }, interpretation: for the power ( kw ) records of all rows , most of them fuzzily match to 5 kw .
Output: no
Command: most_eq { all_rows ; power ( kw ) ; 5 kw }, interpretation: for the power ( kw ) records of all rows , most of them fuzzily match to 5 kw .
Output: yes
Command: most_eq { all_rows ; power ( kw ) ; 5 kw }, interpretation: select the row whose elevation ( m ) record of all rows is maximum . the peak record of this row is mount popomanaseu .
Output: no
Command: eq { hop { argmax { all_rows ; elevation ( m ) } ; peak } ; mount popomanaseu }, interpretation: select the row whose elevation ( m ) record of all rows is maximum . the peak record of this row is mount popomanaseu .
Output: yes
Command: eq { hop { argmax { all_rows ; elevation ( m ) } ; peak } ; mount popomanaseu }, interpretation: select the rows whose winning driver record fuzzily matches to aj foyt . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; winning driver ; aj foyt } } ; 4 }, interpretation: select the rows whose winning driver record fuzzily matches to aj foyt . the number of such rows is 4 .
Output: yes