This paper presents our winning system for SemEval-2026 Task 5 on rating the plausibility of word senses in ambiguous stories. Unlike traditional Word Sense Disambiguation, the task requires predicting continuous plausibility scores that reflect human variability rather than selecting a single correct sense. We propose a multi-target fine-tuning framework for decoder-only large language models that jointly optimizes regression for score prediction and text generation for interpretable explanations. To address inter-annotator variability, we adopt objective-level strategies to enhance robustness. Our system achieves first place, demonstrating the effectiveness of unified regressive–generative modeling for fine-grained plausibility estimation.