<p>Navigating through the complexities of news to extract pivotal events demands innovative methodologies to ensure accurate and reliable outcomes. This paper introduces a novel ensemble approach for event extraction, synergizing multiple models to enhance accuracy and reliability amidst the strategic and operational intricacies of news. A key metric, the Confidence Score (CS), quantifies the reliability and credibility of extracted events, serving as a robust indicator of a model’s assurance in its predictions and providing a mechanism to navigate through ambiguities in the data. The paper offers a holistic representation and estimation of the confidence of individual model outputs. A comprehensive CS evaluation method is introduced, integrating consistency and prior confidence of multi-model output, and is augmented by an iterative update algorithm, enhancing CS evaluation accuracy by comparing consistency of pairwise outputs of identical arguments and updating the CS in a weighted manner based on prior scores. Experiments conducted on a dataset compiled from online news underscore the notable improvement of our method in comparison to both singular model and prevalent ensemble strategies, demonstrating its efficacy and potential applicability in practice.</p>