The purpose of RDF is to enable reasoning ("I'm sharing this with you, and I'm also sharing how to reason about this thing I shared with you, so we can all reas

The purpose of RDF is to enable reasoning ("I'm sharing this with you, and I'm also sharing how to reason about this thing I shared with you, so we can all reason about it the same way"). If you show me an LLM that can take in serialized RDF and perform reasoning on it, I will be surprised. Once the LLM takes in the RDF serialization, it is a dead end for that knowledge: by principle, you can't rely on anything the LLM does with it. In a world of LLMs, it makes much more sense to put the semweb technologies alongside the training step instead. You create ontologies and generate text from triples, then feed the generated text as training for the model. This is good because you can tweak and transform the triples-to-text mechanism in all sorts of ways (you can tune the data while retaining its meaning). It doesn't make much sense to do it now, but if (or when?) training data becomes scarce, converting triples to text might be a viable approach for synthetic data, much more stable than having models themselves generate text.