Word Sense Disambiguation (WSD) is typically framed as a classification task that selects one correct sense for a word. However, real language is often less clear-cut, as a homonym may support several plausible interpretations. SemEval 2026 Task 5 addresses this limitation by introducing plausibility rating, where models estimate how likely each sense is in a narrative context, aligning predictions with graded human judgments. We use GlossBERT and BEM as encoder-based baselines and show that large language models (LLMs) produce more accurate plausibility estimates. Building on this observation, we propose a regression-calibrated LLM model that applies linear regression to adjust raw LLM outputs to better match human annotation patterns. Our calibrated model achieves the highest within-standard-deviation accuracy among our evaluated systems, demonstrating that lightweight post-hoc calibration can substantially improve LLM performance on graded semantic judgment tasks.