Narrative similarity detection involves under-standing the underlying structure of a storyrather than just matching similar words orphrases. This paper details our multi-strategyapproach to the SemEval-2026 Task on Nar-rative Similarity, which requires identifyingwhich of two candidate stories most closelyresembles an anchor story based on three di-mensions: abstract themes, the sequence ofevents, and the final outcomes.We developed three distinct but complemen-tary methods to address this challenge. First,we fine-tuned a specialized story-embeddingmodel using parameter-efficient techniques onsynthetic data. Second, we utilized a "Distill-then-Embed" workflow, where a large languagemodel extracts the essential narrative core ofeach story before computing similarity. Third,we employed direct zero-shot prompting to al-low a high-reasoning model to compare thestories organically.Our analysis reveals that each approach excelsat different types of narrative comparisons, andtheir combination leads to robust performance.We demonstrate the importance of narrative dis-tillation in removing surface-level distractorsand the effectiveness of carefully engineeredprompts in guiding language models to focuson narrative structure