This is partly completely misleading and partly simplified, when it comes to SOTA LLMs.

Subject–Verb–Object triples, POS tagging and dependency structures are not used by LLMs. One of the fundamental differences between modern LLMs and traditional NLP is that heuristics like those are not defined.

And assuming that those specific heuristics are the ones which LLMs would converge on after training is incorrect.

Yes, tokenization and embeddings are exactly how LLMs process input—they break text into tokens and map them to vectors. POS tags and SVOs aren't part of the model pipeline but help visualize structures the models learn implicitly.