SemEval-2026 Task 5 is a shared task on rating the plausibility of an ambiguous homonym in a predetermined context. The dataset of this task consists of a precontext & sentence & ending combinations for each homonym, and the plausibility of the sample is manually rated by 5 annotators. The task of participating teams was to automatically predict the plausibility with respect to the mean rate given by the annotators. Unlike traditional models that rely on single-label selection, this task frames disambiguation as a probabilistic distribution over multiple plausible meanings. To this end, we propose an uncertainty-aware training strategy using GNLL regression, and semantic context enrichment through POS tags and LLM rationales. Our system exhibits competitive performance, achieving 90% accuracy within standard deviation and 81% Spearman correlation, and placing us in the ninth place in the leaderboard.