Rare-disease diagnosis requires converting heterogeneous patient descriptions into a ranked differential over thousands of disorders while keeping each hypothesis tied to verifiable evidence. Phenotype-based tools provide structured, interpretable comparisons but usually require normalized Human Phenotype Ontology (HPO) input. Large language models (LLMs) can read clinical narratives and propose broader hypotheses, yet their free-form outputs are difficult to ground in a disease catalog or combine with ontology-based evidence. We present ORBIT (Orphanet-grounded Retrieval and Bounded LLM Interaction for Traceable rare-disease diagnosis), a catalog-grounded diagnosis layer for both free-text and structured-HPO cases. ORBIT grounds open disease proposals to Orphanet, fuses them with exact phenotype and case-profile retrieval, scores only the resulting candidate pool with a probabilistic model, and reranks a fixed menu of evidence-bearing disease cards. The output records catalog identifiers, candidate provenance, phenotype support, and conflicts. Across RareArena and RareBench, ORBIT improves every reported metric over single-agent and medical-agent systems using the same backbone. Retrieval analysis, ablations, and expert review further identify where complementary candidate sources, iterative refinement, and constrained reranking affect performance.