This paper investigates the ability of Large Language Models (LLMs) to differentiate between canonical and non-canonical sentences in Italian, employing advanced neural architectures like LLaMA and its adaptations. Canonical sentences adhere to the standard Subject-Verb-Object (SVO) structure. We hypothesize that recent generative LLMs are influenced heavily by the English language, where non-canonical structures are very rare. Using the in-context learning technique, we probe these models and further fine-tune them for this specific task. Initial results indicate that these models continue to struggle with this task even after fine-tuning. Additionally, we introduce a new dataset comprising several hundred sentences from the poetry domain, which presents significant challenges for the canonical structure task.