data:image/png;base64,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

false

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 ca:_ sense la primera lletra

All but first of empty word.

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 ca:_ sense la primera paraula

All but first of empty sentence.

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 ca:_ sense la darrera lletra

All but first of empty word.

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 ca:_ sense la darrera paraula

All but last of empty sentence.

pt:a primeira palavra de _ ca:primera paraula de _

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

pt:o último caractere de _ ca:primera lletra de _ 0

pt:a última palavra de _ ca:darrera paraula de _

Last of empty sentence.

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 ca:paraula buida? _ 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 ca:frase buida? _

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 _ firstbffirst1

1bf

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.

ca:frase _

pt:lança o erro _

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

ca:notació textual de _ (

)

Helper function for word/sentence library. Reports its first input, unless that input is empty, in which case it gives its second input as an error message.

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

pt:a primeira palavra de _ ca:primera lletra de _

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