Extracting structured knowledge from unstructured text is a fundamental challenge in machine learning, particularly for concepts organized within complex hierarchical ontologies. In genomics, identifying phenotypes from clinical narratives is crucial for diagnostic precision, yet current methods struggle with contextual interpretation and subtle clinical descriptions. We present a hierarchy-aware workflow for ontology-based phenotype linking that combines semantic and hierarchical signals. Our approach integrates Large Language Models for span detection with retrieval and a hybrid reranking strategy using both Euclidean (semantic) and hyperbolic (hierarchical) embeddings trained on the Human Phenotype Ontology. We show that while hyperbolic embeddings alone do not outperform standard semantic retrieval, they provide complementary structural signals that improve performance over strong baselines when combined with Euclidean representations. In particular, the hybrid approach outperforms existing state-of-the-art methods and yields more hierarchically coherent predictions, especially in settings involving implicit phenotype mentions. Experiments on a public benchmark (ID-68) and a newly released clinical dataset (CHU-50), publicly released with code and data, highlight both performance gains and improved alignment with ontology structure. We further introduce a hierarchy-aware evaluation framework that reflects clinical relevance beyond exact-match metrics.