A study finds that knowledge-graph grounding improves LLMs only when answering questions based on out-of-training facts. On public biomedical knowledge, grounding adds no benefit, but on novel or private data, it boosts accuracy from chance to near-perfect levels, confirming that LLMs rely on external data beyond training for true performance gains.