Narratives are foundational to human expression across cultures. They are in the stories we tell, in folktales, news reports, memoirs, podcasts and visual media. The linguist Bill Labov describes the Narrative as "a recounting of things that have happened, involving a sequence of events meaningfully connected in a temporal and often causal relation, typically structured with a beginning, middle, and end".. We used prompt engineering to develop Large Language Models or LLMs that identify the structural elements of a narrative--in other words, the system automatically extracts the information from a text to answer who did what to whom, where and when and why. We found that LLMs perform quite well on this task for Persian and Sorani Kurdish, especially in inferring implicit information and discontinuous elements, without requiring the integration of NLP pipeline components or structured resources such as WordNet or a Treebank. However, these systems are inconsistent (especially for Kurdish) and don't perform as well in complex analyses such as coreference resolution. For researchers working on endangered or minority languages, this finding opens exciting doors.