True crime podcasts have captivated millions, weaving intricate narratives filled with suspense, mystery, and often, conflicting information. But can artificial intelligence make sense of these complex stories? New research explores how knowledge graph-augmented large language models (KGLLMs) can analyze true crime narratives, like the popular podcast *Serial*, going beyond simple text analysis to understand the connections between people, places, and events. Traditional AI struggles with the nuances of narrative, often misinterpreting information or getting lost in the details. Knowledge graphs offer a solution by organizing information into a structured network, allowing the AI to see the bigger picture. Researchers found that KGLLMs like GraphRAG outperform standard LLMs in answering questions about the podcast, demonstrating a greater understanding of the narrative's complexities. They are also more resilient to “adversarial prompting,” meaning they are less likely to be tricked by false information. Furthermore, KGLLMs provide richer summaries, capturing thematic keywords and emotional context often missed by other AI models. Interestingly, this analysis also highlights the prevalence of hearsay in true crime storytelling, and how it contributes to the emotional impact. The implications extend beyond entertainment. Imagine AI assisting legal professionals by sifting through evidence and identifying inconsistencies, or helping journalists investigate complex stories with multiple perspectives. While there are still challenges to overcome, this research suggests that AI, armed with knowledge graphs, could revolutionize how we analyze and understand complex narratives, both in the world of true crime and beyond.