niryuu/sni-each-converted huggingface dataset:
Command: greater { hop { filter_eq { all_rows ; call sign ; k297al } ; frequency mhz } ; hop { filter_eq { all_rows ; call sign ; k239ax } ; frequency mhz } }, interpretation: for the height records of all rows , most of them are less than 190 .
Output: no
Command: most_less { all_rows ; height ; 190 }, interpretation: for the height records of all rows , most of them are less than 190 .
Output: yes
Command: most_less { all_rows ; height ; 190 }, interpretation: select the rows whose high points record fuzzily matches to damon stoudamire . among these rows , select the rows whose team record fuzzily matches to miami . there is only one such row in the table . the date record of this unqiue row is april 5 .
Output: no
Command: and { only { filter_eq { filter_eq { all_rows ; high points ; damon stoudamire } ; team ; miami } } ; eq { hop { filter_eq { filter_eq { all_rows ; high points ; damon stoudamire } ; team ; miami } ; date } ; april 5 } }, interpretation: select the rows whose high points record fuzzily matches to damon stoudamire . among these rows , select the rows whose team record fuzzily matches to miami . there is only one such row in the table . the date record of this unqiue row is april 5 .
Output: yes
Command: and { only { filter_eq { filter_eq { all_rows ; high points ; damon stoudamire } ; team ; miami } } ; eq { hop { filter_eq { filter_eq { all_rows ; high points ; damon stoudamire } ; team ; miami } ; date } ; april 5 } }, interpretation: select the row whose semifinalists record of all rows is 2nd maximum . the country / territory record of this row is south africa .
Output: no
Command: eq { hop { nth_argmax { all_rows ; semifinalists ; 2 } ; country / territory } ; south africa }, interpretation: select the row whose semifinalists record of all rows is 2nd maximum . the country / territory record of this row is south africa .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; semifinalists ; 2 } ; country / territory } ; south africa }, interpretation: select the row whose year built record of all rows is minimum . the church name record of this row is urnes stavkyrkje .
Output: no
Command: eq { hop { argmin { all_rows ; year built } ; church name } ; urnes stavkyrkje }, interpretation: select the row whose year built record of all rows is minimum . the church name record of this row is urnes stavkyrkje .
Output: yes
Command: eq { hop { argmin { all_rows ; year built } ; church name } ; urnes stavkyrkje }, interpretation: the sum of the silver record of all rows is 2 .
Output: no
Command: round_eq { sum { all_rows ; silver } ; 2 }, interpretation: the sum of the silver record of all rows is 2 .
Output: yes
Command: round_eq { sum { all_rows ; silver } ; 2 }, interpretation: select the rows whose frequency mhz record is less than 100 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_less { all_rows ; frequency mhz ; 100 } } ; 3 }, interpretation: select the rows whose frequency mhz record is less than 100 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_less { all_rows ; frequency mhz ; 100 } } ; 3 }, interpretation: select the rows whose year record is less than or equal to 2009 . the sum of the tournaments played record of these rows is 52 .
Output: no
Command: round_eq { sum { filter_less_eq { all_rows ; year ; 2009 } ; tournaments played } ; 52 }, interpretation: select the rows whose year record is less than or equal to 2009 . the sum of the tournaments played record of these rows is 52 .
Output: yes
Command: round_eq { sum { filter_less_eq { all_rows ; year ; 2009 } ; tournaments played } ; 52 }, interpretation: select the row whose obama % record of all rows is 2nd maximum . the county record of this row is new haven .
Output: no
Command: eq { hop { nth_argmax { all_rows ; obama % ; 2 } ; county } ; new haven }, interpretation: select the row whose obama % record of all rows is 2nd maximum . the county record of this row is new haven .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; obama % ; 2 } ; county } ; new haven }, interpretation: for the category records of all rows , most of them fuzzily match to utilities .
Output: no
Command: most_eq { all_rows ; category ; utilities }, interpretation: for the category records of all rows , most of them fuzzily match to utilities .
Output: yes
Command: most_eq { all_rows ; category ; utilities }, interpretation: select the row whose frequency record of all rows is 2nd minimum . the station record of this row is wgtz - fm .
Output: no
Command: eq { hop { nth_argmin { all_rows ; frequency ; 2 } ; station } ; wgtz - fm }, interpretation: select the row whose frequency record of all rows is 2nd minimum . the station record of this row is wgtz - fm .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; frequency ; 2 } ; station } ; wgtz - fm }, interpretation: select the rows whose reason for change record fuzzily matches to died . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; reason for change ; died } } ; 3 }, interpretation: select the rows whose reason for change record fuzzily matches to died . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; reason for change ; died } } ; 3 }, interpretation: select the rows whose visitor record fuzzily matches to winnipeg . take the date record of this row . select the rows whose visitor record fuzzily matches to chicago . 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 ; winnipeg } ; date } ; hop { filter_eq { all_rows ; visitor ; chicago } ; date } }, interpretation: select the rows whose visitor record fuzzily matches to winnipeg . take the date record of this row . select the rows whose visitor record fuzzily matches to chicago . 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 ; winnipeg } ; date } ; hop { filter_eq { all_rows ; visitor ; chicago } ; date } }, interpretation: select the row whose points record of all rows is maximum . the player record of this row is dave allerdice .
Output: no
Command: eq { hop { argmax { all_rows ; points } ; player } ; dave allerdice }, interpretation: select the row whose points record of all rows is maximum . the player record of this row is dave allerdice .
Output: yes
Command: eq { hop { argmax { all_rows ; points } ; player } ; dave allerdice }, interpretation: select the row whose length record of all rows is 3rd maximum . the year record of this row is 1996 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; length ; 3 } ; year } ; 1996 }, interpretation: select the row whose length record of all rows is 3rd maximum . the year record of this row is 1996 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; length ; 3 } ; year } ; 1996 }, interpretation: for the start source records of all rows , most of them fuzzily match to bbc sport .
Output: no
Command: most_eq { all_rows ; start source ; bbc sport }, interpretation: for the start source records of all rows , most of them fuzzily match to bbc sport .
Output: yes
Command: most_eq { all_rows ; start source ; bbc sport }, interpretation: select the rows whose score record fuzzily matches to 1-1 . there is only one such row in the table . the date record of this unqiue row is march 21 , 2007 .
Output: no
Command: and { only { filter_eq { all_rows ; score ; 1-1 } } ; eq { hop { filter_eq { all_rows ; score ; 1-1 } ; date } ; march 21 , 2007 } }, interpretation: select the rows whose score record fuzzily matches to 1-1 . there is only one such row in the table . the date record of this unqiue row is march 21 , 2007 .
Output: yes
Command: and { only { filter_eq { all_rows ; score ; 1-1 } } ; eq { hop { filter_eq { all_rows ; score ; 1-1 } ; date } ; march 21 , 2007 } }, interpretation: select the rows whose date record is equal to - . there is only one such row in the table . the week record of this unqiue row is 6 .
Output: no
Command: and { only { filter_eq { all_rows ; date ; - } } ; eq { hop { filter_eq { all_rows ; date ; - } ; week } ; 6 } }, interpretation: select the rows whose date record is equal to - . there is only one such row in the table . the week record of this unqiue row is 6 .
Output: yes
Command: and { only { filter_eq { all_rows ; date ; - } } ; eq { hop { filter_eq { all_rows ; date ; - } ; week } ; 6 } }, interpretation: the 1st minimum 2004 - 2005 season record of all rows is 1st in the liga . the club record of the row with 1st minimum 2004 - 2005 season record is benfica .
Output: no
Command: and { eq { nth_min { all_rows ; 2004 - 2005 season ; 1 } ; 1st in the liga } ; eq { hop { nth_argmin { all_rows ; 2004 - 2005 season ; 1 } ; club } ; benfica } }, interpretation: the 1st minimum 2004 - 2005 season record of all rows is 1st in the liga . the club record of the row with 1st minimum 2004 - 2005 season record is benfica .
Output: yes
Command: and { eq { nth_min { all_rows ; 2004 - 2005 season ; 1 } ; 1st in the liga } ; eq { hop { nth_argmin { all_rows ; 2004 - 2005 season ; 1 } ; club } ; benfica } }, interpretation: the average of the overall record record of all rows is 5 .
Output: no
Command: round_eq { avg { all_rows ; overall record } ; 5 }, interpretation: the average of the overall record record of all rows is 5 .
Output: yes
Command: round_eq { avg { all_rows ; overall record } ; 5 }, interpretation: select the rows whose money record is greater than 100000 . there is only one such row in the table . the player record of this unqiue row is ben crenshaw .
Output: no
Command: and { only { filter_greater { all_rows ; money ; 100000 } } ; eq { hop { filter_greater { all_rows ; money ; 100000 } ; player } ; ben crenshaw } }, interpretation: select the rows whose money record is greater than 100000 . there is only one such row in the table . the player record of this unqiue row is ben crenshaw .
Output: yes
Command: and { only { filter_greater { all_rows ; money ; 100000 } } ; eq { hop { filter_greater { all_rows ; money ; 100000 } ; player } ; ben crenshaw } }, interpretation: the average of the attendance record of all rows is 20240 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 20240 }, interpretation: the average of the attendance record of all rows is 20240 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 20240 }, interpretation: select the rows whose player record fuzzily matches to mitchell duke . take the goals record of this row . select the rows whose player record fuzzily matches to john hutchinson . 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 ; player ; mitchell duke } ; goals } ; hop { filter_eq { all_rows ; player ; john hutchinson } ; goals } }, interpretation: select the rows whose player record fuzzily matches to mitchell duke . take the goals record of this row . select the rows whose player record fuzzily matches to john hutchinson . 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 ; player ; mitchell duke } ; goals } ; hop { filter_eq { all_rows ; player ; john hutchinson } ; goals } }, interpretation: select the rows whose fixtures record is equal to 1 . there is only one such row in the table . the round record of this unqiue row is final .
Output: no
Command: and { only { filter_eq { all_rows ; fixtures ; 1 } } ; eq { hop { filter_eq { all_rows ; fixtures ; 1 } ; round } ; final } }, interpretation: select the rows whose fixtures record is equal to 1 . there is only one such row in the table . the round record of this unqiue row is final .
Output: yes
Command: and { only { filter_eq { all_rows ; fixtures ; 1 } } ; eq { hop { filter_eq { all_rows ; fixtures ; 1 } ; round } ; final } }, interpretation: select the rows whose won record is less than or equal to 5 . the sum of the points record of these rows is 75 .
Output: no
Command: round_eq { sum { filter_less_eq { all_rows ; won ; 5 } ; points } ; 75 }, interpretation: select the rows whose won record is less than or equal to 5 . the sum of the points record of these rows is 75 .
Output: yes
Command: round_eq { sum { filter_less_eq { all_rows ; won ; 5 } ; points } ; 75 }, interpretation: for the disease area records of all rows , most of them fuzzily match to cancer .
Output: no
Command: most_eq { all_rows ; disease area ; cancer }, interpretation: for the disease area records of all rows , most of them fuzzily match to cancer .
Output: yes
Command: most_eq { all_rows ; disease area ; cancer }, interpretation: select the rows whose venue record fuzzily matches to western oval . take the crowd record of this row . select the rows whose venue record fuzzily matches to windy hill . take the crowd record of this row . the first record is 3755 larger than the second record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; venue ; western oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; windy hill } ; crowd } } ; 3755 }, interpretation: select the rows whose venue record fuzzily matches to western oval . take the crowd record of this row . select the rows whose venue record fuzzily matches to windy hill . take the crowd record of this row . the first record is 3755 larger than the second record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; venue ; western oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; windy hill } ; crowd } } ; 3755 }, interpretation: the sum of the crowd record of all rows is 124845 .
Output: no
Command: round_eq { sum { all_rows ; crowd } ; 124845 }, interpretation: the sum of the crowd record of all rows is 124845 .
Output: yes
Command: round_eq { sum { all_rows ; crowd } ; 124845 }, interpretation: select the rows whose transfer fee record is greater than 0 . there is only one such row in the table . the player record of this unqiue row is matthew mills .
Output: no
Command: and { only { filter_greater { all_rows ; transfer fee ; 0 } } ; eq { hop { filter_greater { all_rows ; transfer fee ; 0 } ; player } ; matthew mills } }, interpretation: select the rows whose transfer fee record is greater than 0 . there is only one such row in the table . the player record of this unqiue row is matthew mills .
Output: yes
Command: and { only { filter_greater { all_rows ; transfer fee ; 0 } } ; eq { hop { filter_greater { all_rows ; transfer fee ; 0 } ; player } ; matthew mills } }, interpretation: the maximum distance record of all rows is 2500 m .
Output: no
Command: eq { max { all_rows ; distance } ; 2500 m }, interpretation: the maximum distance record of all rows is 2500 m .
Output: yes
Command: eq { max { all_rows ; distance } ; 2500 m }, interpretation: select the rows whose surface record fuzzily matches to carpet ( i ) . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; surface ; carpet ( i ) } } ; 2 }, interpretation: select the rows whose surface record fuzzily matches to carpet ( i ) . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; surface ; carpet ( i ) } } ; 2 }, interpretation: select the row whose race 2 ( pts ) record of all rows is maximum . the country record of this row is france .
Output: no
Command: eq { hop { argmax { all_rows ; race 2 ( pts ) } ; country } ; france }, interpretation: select the row whose race 2 ( pts ) record of all rows is maximum . the country record of this row is france .
Output: yes
Command: eq { hop { argmax { all_rows ; race 2 ( pts ) } ; country } ; france }, 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: for the outcome records of all rows , most of them fuzzily match to winner .
Output: yes
Command: most_eq { all_rows ; outcome ; winner }, interpretation: select the rows whose new adherents per year record is greater than 1000000 . among these rows , select the rows whose growth rate record is greater than 1.70 % . there is only one such row in the table . the religion record of this unqiue row is islam .
Output: no
Command: and { only { filter_greater { filter_greater { all_rows ; new adherents per year ; 1000000 } ; growth rate ; 1.70 % } } ; eq { hop { filter_greater { filter_greater { all_rows ; new adherents per year ; 1000000 } ; growth rate ; 1.70 % } ; religion } ; islam } }, interpretation: select the rows whose new adherents per year record is greater than 1000000 . among these rows , select the rows whose growth rate record is greater than 1.70 % . there is only one such row in the table . the religion record of this unqiue row is islam .
Output: yes
Command: and { only { filter_greater { filter_greater { all_rows ; new adherents per year ; 1000000 } ; growth rate ; 1.70 % } } ; eq { hop { filter_greater { filter_greater { all_rows ; new adherents per year ; 1000000 } ; growth rate ; 1.70 % } ; religion } ; islam } }, interpretation: select the rows whose province record fuzzily matches to santiago de cuba . take the density record of this row . select the rows whose province record fuzzily matches to pinar del río . take the density record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; province ; santiago de cuba } ; density } ; hop { filter_eq { all_rows ; province ; pinar del río } ; density } }, interpretation: select the rows whose province record fuzzily matches to santiago de cuba . take the density record of this row . select the rows whose province record fuzzily matches to pinar del río . take the density record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; province ; santiago de cuba } ; density } ; hop { filter_eq { all_rows ; province ; pinar del río } ; density } }, interpretation: for the under - 11 records of all rows , most of them fuzzily match to not played .
Output: no
Command: most_eq { all_rows ; under - 11 ; not played }, interpretation: for the under - 11 records of all rows , most of them fuzzily match to not played .
Output: yes
Command: most_eq { all_rows ; under - 11 ; not played }, interpretation: select the rows whose away team score record is greater than 10.0 . for the away team score records of these rows , most of them are greater than 10.0 .
Output: no
Command: most_greater { filter_greater { all_rows ; away team score ; 10.0 } ; away team score ; 10.0 }, interpretation: select the rows whose away team score record is greater than 10.0 . for the away team score records of these rows , most of them are greater than 10.0 .
Output: yes
Command: most_greater { filter_greater { all_rows ; away team score ; 10.0 } ; away team score ; 10.0 }, interpretation: select the rows whose platform ( s ) record fuzzily matches to wii . there is only one such row in the table . the game record of this unqiue row is super mario galaxy 2 .
Output: no
Command: and { only { filter_eq { all_rows ; platform ( s ) ; wii } } ; eq { hop { filter_eq { all_rows ; platform ( s ) ; wii } ; game } ; super mario galaxy 2 } }, interpretation: select the rows whose platform ( s ) record fuzzily matches to wii . there is only one such row in the table . the game record of this unqiue row is super mario galaxy 2 .
Output: yes
Command: and { only { filter_eq { all_rows ; platform ( s ) ; wii } } ; eq { hop { filter_eq { all_rows ; platform ( s ) ; wii } ; game } ; super mario galaxy 2 } }, interpretation: select the row whose density record of all rows is maximum . the english name record of this row is xinluo district .
Output: no
Command: eq { hop { argmax { all_rows ; density } ; english name } ; xinluo district }, interpretation: select the row whose density record of all rows is maximum . the english name record of this row is xinluo district .
Output: yes
Command: eq { hop { argmax { all_rows ; density } ; english name } ; xinluo district }, interpretation: select the rows whose city of license record fuzzily matches to ellijay , georgia . take the frequency mhz record of this row . select the rows whose city of license record fuzzily matches to morrow , georgia . take the frequency mhz record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; city of license ; ellijay , georgia } ; frequency mhz } ; hop { filter_eq { all_rows ; city of license ; morrow , georgia } ; frequency mhz } }, interpretation: select the rows whose city of license record fuzzily matches to ellijay , georgia . take the frequency mhz record of this row . select the rows whose city of license record fuzzily matches to morrow , georgia . take the frequency mhz record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; city of license ; ellijay , georgia } ; frequency mhz } ; hop { filter_eq { all_rows ; city of license ; morrow , georgia } ; frequency mhz } }, interpretation: the 3rd maximum crowd record of all rows is 13805 .
Output: no
Command: eq { nth_max { all_rows ; crowd ; 3 } ; 13805 }, interpretation: the 3rd maximum crowd record of all rows is 13805 .
Output: yes
Command: eq { nth_max { all_rows ; crowd ; 3 } ; 13805 }, interpretation: the average of the events record of all rows is 17.75 .
Output: no
Command: round_eq { avg { all_rows ; events } ; 17.75 }, interpretation: the average of the events record of all rows is 17.75 .
Output: yes
Command: round_eq { avg { all_rows ; events } ; 17.75 }, interpretation: select the rows whose population ( in 2008 ) record is less than 1000 . there is only one such row in the table . the suburb record of this unqiue row is o'malley .
Output: no
Command: and { only { filter_less { all_rows ; population ( in 2008 ) ; 1000 } } ; eq { hop { filter_less { all_rows ; population ( in 2008 ) ; 1000 } ; suburb } ; o'malley } }, interpretation: select the rows whose population ( in 2008 ) record is less than 1000 . there is only one such row in the table . the suburb record of this unqiue row is o'malley .
Output: yes
Command: and { only { filter_less { all_rows ; population ( in 2008 ) ; 1000 } } ; eq { hop { filter_less { all_rows ; population ( in 2008 ) ; 1000 } ; suburb } ; o'malley } }, 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: for the surface records of all rows , most of them fuzzily match to clay .
Output: yes
Command: most_eq { all_rows ; surface ; clay }, interpretation: select the rows whose player record fuzzily matches to eddie keher . take the rank record of this row . select the rows whose player record fuzzily matches to philly grimes . take the rank record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; player ; eddie keher } ; rank } ; hop { filter_eq { all_rows ; player ; philly grimes } ; rank } }, interpretation: select the rows whose player record fuzzily matches to eddie keher . take the rank record of this row . select the rows whose player record fuzzily matches to philly grimes . take the rank record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; player ; eddie keher } ; rank } ; hop { filter_eq { all_rows ; player ; philly grimes } ; rank } }, interpretation: the average of the international tourism expenditure 2011 record of all rows is 45-48 billion .
Output: no
Command: round_eq { avg { all_rows ; international tourism expenditure 2011 } ; 45-48 billion }, interpretation: the average of the international tourism expenditure 2011 record of all rows is 45-48 billion .
Output: yes
Command: round_eq { avg { all_rows ; international tourism expenditure 2011 } ; 45-48 billion }, interpretation: select the rows whose away team score record is less than 10 . select the row whose crowd record of these rows is 2nd maximum . the venue record of this row is victoria park .
Output: no
Command: eq { hop { nth_argmax { filter_less { all_rows ; away team score ; 10 } ; crowd ; 2 } ; venue } ; victoria park }, interpretation: select the rows whose away team score record is less than 10 . select the row whose crowd record of these rows is 2nd maximum . the venue record of this row is victoria park .
Output: yes
Command: eq { hop { nth_argmax { filter_less { all_rows ; away team score ; 10 } ; crowd ; 2 } ; venue } ; victoria park }, interpretation: select the row whose location attendance record of all rows is 2nd maximum . the date record of this row is january 12 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; location attendance ; 2 } ; date } ; january 12 }, interpretation: select the row whose location attendance record of all rows is 2nd maximum . the date record of this row is january 12 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; location attendance ; 2 } ; date } ; january 12 }, interpretation: select the rows whose visitor record does not match to toronto . among these rows , select the rows whose points record is equal to 28 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_not_eq { all_rows ; visitor ; toronto } ; points ; 28 } } ; 2 }, interpretation: select the rows whose visitor record does not match to toronto . among these rows , select the rows whose points record is equal to 28 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_not_eq { all_rows ; visitor ; toronto } ; points ; 28 } } ; 2 }, interpretation: select the rows whose competition record fuzzily matches to friendly . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; competition ; friendly } } ; 4 }, interpretation: select the rows whose competition record fuzzily matches to friendly . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; competition ; friendly } } ; 4 }, interpretation: select the row whose commenced operations record of all rows is 2nd minimum . the airlines record of this row is silkair .
Output: no
Command: eq { hop { nth_argmin { all_rows ; commenced operations ; 2 } ; airlines } ; silkair }, interpretation: select the row whose commenced operations record of all rows is 2nd minimum . the airlines record of this row is silkair .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; commenced operations ; 2 } ; airlines } ; silkair }, interpretation: select the rows whose date record fuzzily matches to january 29 . take the high points record of this row . select the rows whose date record fuzzily matches to january 1 . take the high points record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; date ; january 29 } ; high points } ; hop { filter_eq { all_rows ; date ; january 1 } ; high points } }, interpretation: select the rows whose date record fuzzily matches to january 29 . take the high points record of this row . select the rows whose date record fuzzily matches to january 1 . take the high points record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; date ; january 29 } ; high points } ; hop { filter_eq { all_rows ; date ; january 1 } ; high points } }, interpretation: select the rows whose name record is arbitrary . the number of such rows is 17 .
Output: no
Command: eq { count { filter_all { all_rows ; name } } ; 17 }, interpretation: select the rows whose name record is arbitrary . the number of such rows is 17 .
Output: yes
Command: eq { count { filter_all { all_rows ; name } } ; 17 }, interpretation: for the surface records of all rows , most of them fuzzily match to hard .
Output: no
Command: most_eq { all_rows ; surface ; hard }, interpretation: the average of the best score record of all rows is 37 .
Output: no
Command: round_eq { avg { all_rows ; best score } ; 37 }, interpretation: the average of the best score record of all rows is 37 .
Output: yes
Command: round_eq { avg { all_rows ; best score } ; 37 }, interpretation: select the rows whose date record fuzzily matches to october . for the result records of these rows , most of them fuzzily match to l .
Output: no
Command: most_eq { filter_eq { all_rows ; date ; october } ; result ; l }, interpretation: select the rows whose date record fuzzily matches to october . for the result records of these rows , most of them fuzzily match to l .
Output: yes
Command: most_eq { filter_eq { all_rows ; date ; october } ; result ; l }, 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: for the played records of all rows , all of them are equal to 30 .
Output: yes
Command: all_eq { all_rows ; played ; 30 }, interpretation: select the rows whose car make record fuzzily matches to pontiac . there is only one such row in the table . the driver record of this unqiue row is tony stewart .
Output: no
Command: and { only { filter_eq { all_rows ; car make ; pontiac } } ; eq { hop { filter_eq { all_rows ; car make ; pontiac } ; driver } ; tony stewart } }, interpretation: select the rows whose car make record fuzzily matches to pontiac . there is only one such row in the table . the driver record of this unqiue row is tony stewart .
Output: yes
Command: and { only { filter_eq { all_rows ; car make ; pontiac } } ; eq { hop { filter_eq { all_rows ; car make ; pontiac } ; driver } ; tony stewart } }, interpretation: the average of the total flights record of all rows is 16.63 .
Output: no
Command: round_eq { avg { all_rows ; total flights } ; 16.63 }, interpretation: the average of the total flights record of all rows is 16.63 .
Output: yes
Command: round_eq { avg { all_rows ; total flights } ; 16.63 }, interpretation: select the rows whose result record fuzzily matches to new seat democratic - republican gain . there is only one such row in the table . the district record of this unqiue row is north carolina 9 .
Output: no
Command: and { only { filter_eq { all_rows ; result ; new seat democratic - republican gain } } ; eq { hop { filter_eq { all_rows ; result ; new seat democratic - republican gain } ; district } ; north carolina 9 } }, interpretation: select the rows whose result record fuzzily matches to new seat democratic - republican gain . there is only one such row in the table . the district record of this unqiue row is north carolina 9 .
Output: yes
Command: and { only { filter_eq { all_rows ; result ; new seat democratic - republican gain } } ; eq { hop { filter_eq { all_rows ; result ; new seat democratic - republican gain } ; district } ; north carolina 9 } }, interpretation: for the film or series records of all rows , most of them fuzzily match to eastenders .
Output: no
Command: most_eq { all_rows ; film or series ; eastenders }, interpretation: for the film or series records of all rows , most of them fuzzily match to eastenders .
Output: yes
Command: most_eq { all_rows ; film or series ; eastenders }, interpretation: select the rows whose country record fuzzily matches to scotland . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; country ; scotland } } ; 2 }, interpretation: select the rows whose country record fuzzily matches to scotland . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; country ; scotland } } ; 2 }, interpretation: select the rows whose year record fuzzily matches to 1959 . take the rank record of this row . select the rows whose year record fuzzily matches to 1958 . take the rank record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; year ; 1959 } ; rank } ; hop { filter_eq { all_rows ; year ; 1958 } ; rank } }, interpretation: select the rows whose year record fuzzily matches to 1959 . take the rank record of this row . select the rows whose year record fuzzily matches to 1958 . take the rank record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; year ; 1959 } ; rank } ; hop { filter_eq { all_rows ; year ; 1958 } ; rank } }, interpretation: the maximum date record of all rows is july 30 , 2008 . the region record of the row with superlative date record is japan .
Output: no
Command: and { eq { max { all_rows ; date } ; july 30 , 2008 } ; eq { hop { argmax { all_rows ; date } ; region } ; japan } }, interpretation: the maximum date record of all rows is july 30 , 2008 . the region record of the row with superlative date record is japan .
Output: yes
Command: and { eq { max { all_rows ; date } ; july 30 , 2008 } ; eq { hop { argmax { all_rows ; date } ; region } ; japan } }, interpretation: select the rows whose player record fuzzily matches to billy casper . take the year ( s ) won record of this row . select the rows whose player record fuzzily matches to arnold palmer . take the year ( s ) won record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; player ; billy casper } ; year ( s ) won } ; hop { filter_eq { all_rows ; player ; arnold palmer } ; year ( s ) won } }, interpretation: select the rows whose player record fuzzily matches to billy casper . take the year ( s ) won record of this row . select the rows whose player record fuzzily matches to arnold palmer . take the year ( s ) won record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; player ; billy casper } ; year ( s ) won } ; hop { filter_eq { all_rows ; player ; arnold palmer } ; year ( s ) won } }, interpretation: select the rows whose date successor seated record fuzzily matches to november . among these rows , select the rows whose successor record fuzzily matches to ( r ) . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; date successor seated ; november } ; successor ; ( r ) } } ; 2 }, interpretation: select the rows whose date successor seated record fuzzily matches to november . among these rows , select the rows whose successor record fuzzily matches to ( r ) . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; date successor seated ; november } ; successor ; ( r ) } } ; 2 }, interpretation: select the row whose difference record of all rows is 2nd maximum . the team record of this row is americano - sp .
Output: no
Command: eq { hop { nth_argmax { all_rows ; difference ; 2 } ; team } ; americano - sp }, interpretation: select the row whose difference record of all rows is 2nd maximum . the team record of this row is americano - sp .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; difference ; 2 } ; team } ; americano - sp }, interpretation: for the gender records of all rows , all of them fuzzily match to coed .
Output: no
Command: all_eq { all_rows ; gender ; coed }, interpretation: for the gender records of all rows , all of them fuzzily match to coed .
Output: yes
Command: all_eq { all_rows ; gender ; coed }, interpretation: select the rows whose venue record is arbitrary . the number of such rows is 6 .
Output: no
Command: eq { count { filter_all { all_rows ; venue } } ; 6 }, interpretation: select the rows whose venue record is arbitrary . the number of such rows is 6 .
Output: yes
Command: eq { count { filter_all { all_rows ; venue } } ; 6 }, interpretation: select the rows whose tournament record fuzzily matches to world cross country championships . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; tournament ; world cross country championships } } ; 3 }, interpretation: select the rows whose tournament record fuzzily matches to world cross country championships . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; tournament ; world cross country championships } } ; 3 }, interpretation: select the rows whose song record fuzzily matches to endless love . take the weeks on top record of this row . select the rows whose song record fuzzily matches to morning train . take the weeks on top record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; song ; endless love } ; weeks on top } ; hop { filter_eq { all_rows ; song ; morning train } ; weeks on top } }, interpretation: select the rows whose song record fuzzily matches to endless love . take the weeks on top record of this row . select the rows whose song record fuzzily matches to morning train . take the weeks on top record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; song ; endless love } ; weeks on top } ; hop { filter_eq { all_rows ; song ; morning train } ; weeks on top } }, interpretation: the average of the total prize money record of all rows is 135000 .
Output: no
Command: round_eq { avg { all_rows ; total prize money } ; 135000 }, interpretation: the average of the total prize money record of all rows is 135000 .
Output: yes
Command: round_eq { avg { all_rows ; total prize money } ; 135000 }, interpretation: select the rows whose tournament record fuzzily matches to world championships . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; tournament ; world championships } } ; 2 }, interpretation: select the rows whose tournament record fuzzily matches to world championships . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; tournament ; world championships } } ; 2 }, interpretation: select the rows whose date record fuzzily matches to 2006 . select the row whose date record of these rows is minimum . the opponent record of this row is josipa bek .
Output: no
Command: eq { hop { argmin { filter_eq { all_rows ; date ; 2006 } ; date } ; opponent } ; josipa bek }, interpretation: select the rows whose date record fuzzily matches to 2006 . select the row whose date record of these rows is minimum . the opponent record of this row is josipa bek .
Output: yes
Command: eq { hop { argmin { filter_eq { all_rows ; date ; 2006 } ; date } ; opponent } ; josipa bek }, interpretation: select the rows whose home team record fuzzily matches to carlton . take the home team score record of this row . select the rows whose home team record fuzzily matches to footscray . take the home team score record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; home team ; carlton } ; home team score } ; hop { filter_eq { all_rows ; home team ; footscray } ; home team score } }, interpretation: select the rows whose home team record fuzzily matches to carlton . take the home team score record of this row . select the rows whose home team record fuzzily matches to footscray . take the home team score record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; home team ; carlton } ; home team score } ; hop { filter_eq { all_rows ; home team ; footscray } ; home team score } }, interpretation: select the rows whose venue record fuzzily matches to sydney cricket ground . take the date record of this row . select the rows whose venue record fuzzily matches to waca ground . take the date record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; venue ; sydney cricket ground } ; date } ; hop { filter_eq { all_rows ; venue ; waca ground } ; date } }, interpretation: select the rows whose venue record fuzzily matches to sydney cricket ground . take the date record of this row . select the rows whose venue record fuzzily matches to waca ground . take the date record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; venue ; sydney cricket ground } ; date } ; hop { filter_eq { all_rows ; venue ; waca ground } ; date } }, interpretation: select the row whose goals record of all rows is maximum . the player record of this row is oleg veretennikov .
Output: no
Command: eq { hop { argmax { all_rows ; goals } ; player } ; oleg veretennikov }, interpretation: select the row whose goals record of all rows is maximum . the player record of this row is oleg veretennikov .
Output: yes
Command: eq { hop { argmax { all_rows ; goals } ; player } ; oleg veretennikov }, interpretation: select the rows whose leading lady record fuzzily matches to alberta vaughn . there is only one such row in the table . the title record of this unqiue row is randy rides alone .
Output: no
Command: and { only { filter_eq { all_rows ; leading lady ; alberta vaughn } } ; eq { hop { filter_eq { all_rows ; leading lady ; alberta vaughn } ; title } ; randy rides alone } }, interpretation: select the rows whose leading lady record fuzzily matches to alberta vaughn . there is only one such row in the table . the title record of this unqiue row is randy rides alone .
Output: yes
Command: and { only { filter_eq { all_rows ; leading lady ; alberta vaughn } } ; eq { hop { filter_eq { all_rows ; leading lady ; alberta vaughn } ; title } ; randy rides alone } }, interpretation: the average of the sales record of all rows is 1236000 .
Output: no
Command: round_eq { avg { all_rows ; sales } ; 1236000 }, interpretation: the average of the sales record of all rows is 1236000 .
Output: yes
Command: round_eq { avg { all_rows ; sales } ; 1236000 }, interpretation: select the rows whose class record fuzzily matches to a . there is only one such row in the table .
Output: no
Command: only { filter_eq { all_rows ; class ; a } }, interpretation: select the rows whose class record fuzzily matches to a . there is only one such row in the table .
Output: yes
Command: only { filter_eq { all_rows ; class ; a } }, interpretation: select the row whose caps record of all rows is 3rd maximum . the player record of this row is kyle beckerman .
Output: no
Command: eq { hop { nth_argmax { all_rows ; caps ; 3 } ; player } ; kyle beckerman }, interpretation: select the row whose caps record of all rows is 3rd maximum . the player record of this row is kyle beckerman .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; caps ; 3 } ; player } ; kyle beckerman }, interpretation: select the rows whose player record fuzzily matches to ian smart . take the rating record of this row . select the rows whose player record fuzzily matches to dave dickenson . take the rating record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; player ; ian smart } ; rating } ; hop { filter_eq { all_rows ; player ; dave dickenson } ; rating } }, interpretation: select the rows whose player record fuzzily matches to ian smart . take the rating record of this row . select the rows whose player record fuzzily matches to dave dickenson . take the rating record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; player ; ian smart } ; rating } ; hop { filter_eq { all_rows ; player ; dave dickenson } ; rating } }, 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: for the party records of all rows , most of them fuzzily match to republican .
Output: yes
Command: most_eq { all_rows ; party ; republican }, interpretation: select the rows whose chassis record fuzzily matches to ferrari 156 aero . there is only one such row in the table . the year record of this unqiue row is 1964 .
Output: no
Command: and { only { filter_eq { all_rows ; chassis ; ferrari 156 aero } } ; eq { hop { filter_eq { all_rows ; chassis ; ferrari 156 aero } ; year } ; 1964 } }, interpretation: select the rows whose chassis record fuzzily matches to ferrari 156 aero . there is only one such row in the table . the year record of this unqiue row is 1964 .
Output: yes
Command: and { only { filter_eq { all_rows ; chassis ; ferrari 156 aero } } ; eq { hop { filter_eq { all_rows ; chassis ; ferrari 156 aero } ; year } ; 1964 } }, interpretation: select the row whose against record of all rows is minimum . the opposing teams record of this row is ireland .
Output: no
Command: eq { hop { argmin { all_rows ; against } ; opposing teams } ; ireland }, interpretation: select the row whose against record of all rows is minimum . the opposing teams record of this row is ireland .
Output: yes
Command: eq { hop { argmin { all_rows ; against } ; opposing teams } ; ireland }, 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: for the type records of all rows , all of them fuzzily match to private .
Output: yes
Command: all_eq { all_rows ; type ; private }, interpretation: select the rows whose location attendance record fuzzily matches to united center . the sum of the location attendance record of these rows is 226050 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; location attendance ; united center } ; location attendance } ; 226050 }, interpretation: select the rows whose location attendance record fuzzily matches to united center . the sum of the location attendance record of these rows is 226050 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; location attendance ; united center } ; location attendance } ; 226050 }, interpretation: select the row whose lost record of all rows is maximum . the team record of this row is lpż zielona góra .
Output: no
Command: eq { hop { argmax { all_rows ; lost } ; team } ; lpż zielona góra }, interpretation: select the row whose lost record of all rows is maximum . the team record of this row is lpż zielona góra .
Output: yes
Command: eq { hop { argmax { all_rows ; lost } ; team } ; lpż zielona góra }, interpretation: for the date records of all rows , all of them fuzzily match to 11 april 1981 .
Output: no
Command: all_eq { all_rows ; date ; 11 april 1981 }, interpretation: for the date records of all rows , all of them fuzzily match to 11 april 1981 .
Output: yes
Command: all_eq { all_rows ; date ; 11 april 1981 }, interpretation: select the row whose home team score record of all rows is maximum . the home team record of this row is essendon .
Output: no
Command: eq { hop { argmax { all_rows ; home team score } ; home team } ; essendon }, interpretation: select the row whose home team score record of all rows is maximum . the home team record of this row is essendon .
Output: yes
Command: eq { hop { argmax { all_rows ; home team score } ; home team } ; essendon }, interpretation: for the subregion records of all rows , most of them fuzzily match to baixo vouga .
Output: no
Command: most_eq { all_rows ; subregion ; baixo vouga }, interpretation: for the subregion records of all rows , most of them fuzzily match to baixo vouga .
Output: yes
Command: most_eq { all_rows ; subregion ; baixo vouga }, interpretation: select the rows whose challenge winner record fuzzily matches to food . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; challenge winner ; food } } ; 4 }, interpretation: select the rows whose challenge winner record fuzzily matches to food . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; challenge winner ; food } } ; 4 }, interpretation: select the row whose enrollment record of all rows is 1st maximum . the school record of this row is carson .
Output: no
Command: eq { hop { nth_argmax { all_rows ; enrollment ; 1 } ; school } ; carson }, interpretation: select the row whose enrollment record of all rows is 1st maximum . the school record of this row is carson .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; enrollment ; 1 } ; school } ; carson }, interpretation: select the rows whose district record fuzzily matches to fregenal de la sierra . the maximum election date record of these rows is 1853 - 2 - 4 .
Output: no