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data:image/png;base64,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

true

Takes a text string as input, and reports a new text string containing all but the first character of the input.

pt:_ sem o primeiro caractere resulti2

Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the first word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE->LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)

pt:_ sem a primeira palavra

Takes a text string as input, and reports a new text string containing all but the last letter of the input.

pt:_ sem o último caractere resulti1

Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the last word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE->LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)

pt:_ sem a última palavra 1 1 11 1

pt:a primeira palavra de _

Takes a text string as input, and reports the last character in the string.

pt:o último caractere de _

pt:a última palavra de _ 1 1

Takes a text string as input, and reports TRUE if the string has no characters in it of any kind, otherwise false.

pt:a palavra _ está vazia

Takes a text string as input, and reports TRUE if the input contains no characters other than spaces (therefore, no words when the string is considered as a sentence), otherwise FALSE.

pt:a frase _ está vazia

de:füge Wörter zusammen _ ca:uneix les paraules _ es:unir las palabras _ fr:fusionne les mots _ pt:uma frase com as palavras _ 121

de:Liste $arrowRight Satz _ ca:llista $arrowRight frase _ es:lista $arrowRight frase _ fr:liste $arrowRight phrase _ pt:uma frase com as palavras em _

de:Satz $arrowRight Liste _ ca:frase $arrowRight llista _ es:frase $arrowRight lista _ fr:phrase $arrowRight liste _ pt:uma lista com as palavras da frase _

report a list in which each item is one letter from the input word

de:Wort $arrowRight Liste _ ca:paraula $arrowRight llista _ es:palabra $arrowRight lista _ fr:mot $arrowRight liste _ pt:uma lista com os caracteres da palavra _

join all the items of the input list into a single word, and report it

de:Liste $arrowRight Wort _ ca:llista $arrowRight paraula _ es:lista $arrowRight palabra _ fr:liste $arrowRight mot _ pt:uma palavra com os caracteres em _

89,67,5,90,-2,0,190,8890 -2 0 190 8