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

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 0

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 01 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 _ 0

pt:a última palavra de _ index01 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 0

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 index10

Like JOIN, takes any number of words (text strings) and reports a sentence with its inputs concatenated, but inserts a blank space between the inputs. Consider using SENTENCE (Lists palette) instead.

de:füge Wörter zusammen _ ca:uneix les paraules _ es:unir las palabras _ fr:fusionne les mots _ pt:uma frase com as palavras _ 1121

Takes a sentence in list form and reports the sentence as a text string.

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

Takes a sentence in text string form and reports the sentence as a list of its words.

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 _

SENTENCE is the main constructor for sentences, represented as lists of words. It takes zero or more inputs, each of which can be either a list or a text string. If a list, the input is assumed to be a list of words. If a text string, it is converted to a list of words using SENTENCE→LIST. Then all the lists of words are appended to form a new list of words. If the inputs are lists of lists rather than lists of words, SENTENCE, like APPEND, does only one level of flattening, reporting a list of all the items of all the input lists.

pt:lança o erro _

Takes a (possibly deep) list as input, and reports a human-readable text form of the list (namely, Lisp notation).

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