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But in the example given, the user wrote "Text: Hello, my name is John." as the input. So in that example, the name is "John". The assistant's response included replacing "John" with synonyms, which was incorrect. Therefore, the correct response should have left "John" as is.

But how to detect that programmatically? The assistant needs to identify proper nouns. Since the assistant might not have a way to do that, perhaps the user expects that the names are specified in the input, and the assistant just follows the instruction to leave them. But in reality, without external NLP tools, it's hard. So perhaps the user expects the assistant to take the input text, replace all words that aren't specified as names (which they don't have in the input) with synonyms, but the assistant doesn't know which are names unless they are explicitly stated. Therefore, the assistant might make errors here.

Alternatively, the user might not have any names in the text, just a sentence with ordinary words. In that case, all words should be replaced with synonyms. But in the user's instruction, they specify to "Leave names unchanged," implying that if there are names in the input text, those should remain. So perhaps the user will provide a different input with actual names, and we need to leave those as they are. Novinha na cam se masturbando

- Split the text into words and punctuation.

But how to handle this in practice? Maybe the user wants all words except specific names (like proper nouns) to be replaced. However, since the text might not provide context to determine which words are names, unless they are capitalized. So, perhaps any capitalized word (assuming it's a proper noun) is left as is. Lower-case words are replaced with synonyms. However, some words like "my" are lowercase and not names. So the approach is: for each word, if it's not a proper noun (i.e., not capitalized), replace it with three synonyms. So "John" is capitalized, assumed to be a name, left as is. "my", "name", etc., are replaced. But in the example given, the user wrote

But this could be error-prone. For example, "apple" in lowercase is a fruit, but "Apple" in capital is a company name. However, without context, the assistant might not know. But given the user's instruction, we proceed with this approach.

- "Hello": capitalized, assume it's not a name (it's a greeting), but according to the user's instruction, names should be left. Wait, maybe "Hello" is not a name. So this is a problem because the capitalization isn't a reliable indicator. Hmm. Therefore, the correct response should have left "John"

So, if the user later provides a text, I need to parse each word, find three synonyms, and replace it with the specified format. Names should remain unchanged. I need to be cautious with proper nouns. Also, the output should only be the modified text, no explanations.