Kamera 10 Vjecare Masturbon Ne Karrige Vajza Pe... | 2025 |

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.

Potential issues: Words that are names but look like common nouns. For example, "Apple" could be a company name or a fruit. Without context, it's hard to tell. However, the user wants names kept, so if it's a known name, it stays. Otherwise, replace with synonyms. So maybe rely on capitalization, but that's not foolproof. Kamera 10 vjecare Masturbon ne karrige Vajza Pe...

Let's take the example sentence. "The" is an article; names here are "fox" and "dog" (common nouns, not names). So "quick" would be replaced with spry, "brown" with ochre, etc. But I need to be careful not to replace any proper nouns. For instance, if there's a name like "John," it stays as is. But how to distinguish between common and proper

The user says "modify all words with 3 alternatives using syn3 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. For example, "Amazon" is a proper noun, so

So, the key challenges are correctly identifying names and finding accurate synonyms. Since the user wants the result only, after processing, the model should output the transformed text with synonyms in the specified format, keeping names unchanged.