In this paper, we report our system for SemEval-2026 Task 5, which predicts graded plausibility scores for target word senses in narrative context. We explore embedding-based similarity, transformer fine tuning, and a three-stage curriculum combining WiC pretraining, Wasserstein distribution learning, and KL-based calibration. Our best model, DeBERTa-XLarge with curriculum training, achieves 78% accu-racy within one standard deviation and a Spear-man correlation of 0.70, with an overall test score of 0.74. Results show that distribution modeling better aligns with human plausibility judgments than single-score prediction