ConceptE introduces a framework that uses large language models to derive concept-level semantics from event triggers, enabling more coherent event clustering and reliable hierarchy expansion. Experiments on ACE, ERE, and MAVEN show ConceptE outperforms existing methods, with up to 12.37\% improvement in BCubed-F1 and 6.48\% in Taxo_F1.