Large Language models (LLMs) are increasingly integrated into everyday applications. As their influence grows, understanding their decision-making and underlying personality becomes essential. In this work, we interpret model personality using our proposed \textit{Supernova Event} Dataset, a novel dataset with diverse articles spanning biographies, historical events, news, and scientific discoveries. We use this dataset to benchmark LLMs on extracting and ranking key events from text, a subjective and complex challenge that requires reasoning over long-range context and modeling causal chains. We evaluate small models like Phi-4, Orca 2, and Qwen 2.5, and large, stronger models such as Claude 3.7, Gemini 2.5, and Open-AI o3, and propose a framework where another LLM acts as a judge to infer each model’s personality based on its selection and classification of events. Our analysis shows distinct personality traits: for instance, Orca 2 demonstrates emotional reasoning focusing on interpersonal dynamics, while Qwen 2.5 displays a more strategic, analytical style. When analyzing scientific discovery events, Claude Sonnet 3.7 emphasizes conceptual framing, Gemini 2.5 Pro prioritizes empirical validation, and o3 favors step-by-step causal reasoning. Our analysis improves model interpretability, making them user-friendly for a wide range of diverse applications. \textit{Supernova Event} Dataset is publicly available at this \href{https://huggingface.co/datasets/SupernovaEvent/SupernovaEventDataset}{link}.