Knowledge graph embedding (KGE) models are designed for the task of link prediction, which aims to infer missing triples by learning accurate representations for entities and relations within a knowledge graph. However, existing KGE research largely overlooks the issue of probability calibration, leading to uncalibrated probability estimates that fail to reflect the true correctness of predicted triples, potentially resulting in erroneous decisions. Moreover, current calibration methods are not well-suited for KGE models, and no dedicated probability calibration method has been specifically designed for them. In this paper, we propose KGE Calibrator (KGEC), the first probability calibration method tailored for KGE models to enhance the trustworthiness of their predictions. To achieve this, we introduce a Jump Selection Strategy that improves efficiency by selecting the most informative instances while filtering out less significant ones. We also propose Multi-Binning Scaling, which models different probability levels separately to increase the model’s capacity and flexibility. Additionally, we propose a Wasserstein distance-based loss function to further boost calibration performance. Extensive experiments across multiple datasets demonstrate that KGEC consistently outperforms existing calibration methods in terms of both effectiveness and efficiency, making it a promising solution for probability calibration in KGE models.