Researchers propose entity-level membership inference to determine if an LLM has been exposed to information about a real-world entity during training. By constructing prompts with limited entity clues and analyzing semantic features in generated responses, their five interrogation strategies achieve up to 0.97 AUC and improve Balanced Accuracy by 6.0%–17.5% over adapted baselines on person entities.