A motif is a pattern that appears across stories, tales, or legends, such as “a prince falls in love with commoner”. We propose a method based on large language models to extract such motifs automatically from stories. Our approach addresses challenges such as motif synonymy, and achieves high alignment with expert-annotated ground truth. We illustrate the utility of our extraction in downstream applications such as mitigating gender bias in existing motif collections, studying narrative event sequences, quantifying cross-regional story similarity, validating the correlation between linguistically related cultures, and tracing the relatedness of religious traditions.