This paper describes our approach and submission to the SemEval 2025 shared task on “Multilingual Characterization and Extraction of Narratives from Online News”. The purpose of this task was to assign primary and fine-grained roles to named entities in news articles from five different languages, on the topics of Climate Change and Ukraine-Russia War. In this paper, we explain how we approached the task by utilizing multiple LLMs via Prompt Engineering and combining their results into a final task result through an ensemble meta-classification technique. Our experimental results demonstrate that this integrated approach outperforms the provided baseline in detecting bias, deception, and manipulation in news media across multiple languages.