Next, for each non-name word, find three synonyms. I'll need to use a thesaurus or an API to get synonyms. If a word doesn't have three synonyms, maybe use the closest possible or note that. But since the user wants exactly three, I have to ensure that.
1. Split the input text into words. 2. For each word, check if it's a proper noun (capitalized). 3. If it's a proper noun, leave it. 4. If not, find three synonyms. 5. Format each with syn1. 6. Combine the words back into the output text.
Okay, let's see. The user wants me to modify all words in the text with three alternatives each, using the syn2 format. But they mentioned to keep names intact. So first, I need to identify all the words in the text and check which ones are names. Since the example text provided is empty, maybe it's a placeholder. Let me think about the process. Kamera 10 vjecare Masturbon ne karrige Vajza Pe...
But how to distinguish between common and proper nouns without more context? Maybe based on capitalization and presence in a name database. Since I can't access external databases, I'll have to make educated guesses. For example, "Amazon" is a proper noun, so it remains; "river" is a common noun, so replace with synonyms.
"result": ""
Another thing: Some words might not have three synonyms. For example, "jumps" could be replaced with "leaps, springs, bounds." But if the word is less common, finding three might be challenging. In that case, use the best available options.
Also, ensuring that the output is only the modified text without any extra explanation. So the model needs to process each word systematically, check for names, and apply synonyms where possible. Let me outline the steps again: Next, for each non-name word, find three synonyms
The user says "modify all words with 3 alternatives using syn1 format. Keep names intact. Only the result." So assuming the input text is given, like "The quick brown fox jumps over the lazy dog," I need to process each word except names.