This paper presents a state-merging algorithm for learning timed languages definable by Event-Recording Automata (ERA) using positive and negative samples in the form of symbolic timed words. Our algorithm, \(\textsf {LEAP}\) (Learning Event-recording Automata Passively), constructs a possibly nondeterministic ERA from such samples based on merging techniques. We prove that determining whether two ERA states can be merged while preserving sample consistency is an \(\textsf{NP}\) -complete problem, and address this with a practical SMT-based solution. Our implementation demonstrates the algorithm’s effectiveness through examples. We also show that every ERA-definable language can be inferred using our algorithm with a suitable sample.