Imagine a world where AI could truly understand narratives, identifying key characters and plot devices without explicit instructions. That's the promise of Major Entity Identification (MEI), a groundbreaking new approach that's transforming how we think about coreference resolution. Traditional coreference resolution models have struggled to generalize across different writing styles and domains, often stumbling over variations in how entities are mentioned. MEI tackles this head-on by focusing on identifying only the most frequent entities, which are usually the central figures in a story or discussion. By pre-defining these "major entities," the AI can focus its efforts on accurately linking related mentions, like pronouns or alternative names, without getting bogged down by less important references. The result is a dramatic improvement in accuracy and efficiency, opening doors to a wide range of applications. Researchers have shown that MEI models outperform traditional coreference systems, especially on literary datasets and lengthy texts. This superior performance is attributed to MEI's ability to adapt quickly to different contexts and avoid errors caused by the variability of coreference annotations. But the innovation doesn't stop there. MEI's streamlined approach is also unlocking the potential of Large Language Models (LLMs) for complex coreference tasks. By incorporating MEI principles into prompt engineering, LLMs like GPT-4 can achieve near-human accuracy in identifying major entities within a narrative. This opens up exciting new possibilities for AI-driven content analysis, personalized reading experiences, and even automated story generation. While MEI still has room to grow, its potential to revolutionize how AI interacts with language is clear. By focusing on the heart of the narrative, MEI is not just improving accuracy, but also making AI-powered narrative understanding more practical, scalable, and accessible than ever before.