Interpreting Noun-Noun Compounds remains a persistent challenge for Large Language Models (LLMs) because the semantic relation between the modifier and the head is rarely stated explicitly. Recent benchmarks frame Noun-Noun Compound Interpretation as a multiple-choice question. This setting, although prompts LLMs to yield more controlled results, still suffer from two main limitations: vague relation descriptions and failure to handle polysemous compounds. We introduce a dual-faceted textual enrichment framework that augments prompts. Description enrichment paraphrases relations into event‑oriented descriptions instantiated with the target compound to explicitly surface the hidden event connecting head and modifier. Conditioned enrichment identifies polysemous compounds leveraging qualia-role binding and assigns each compound with condition cues for disambiguation. Our method yields consistently higher accuracy across three LLM families. These gains suggest that surfacing latent compositional structure and contextual constraint is a promising path toward deeper semantic understanding in language models. The data and codebase will be made publicly available.