This paper describes our system for SemEval-2026 Task 5, which focuses on predicting the plausibility of word senses in ambiguous narrative contexts. The task requires assigning a real-valued plausibility score to candidate word senses based on aggregated human judgments. Our approach compares two modeling paradigms: (i) a pretrained transformer-based regression model using DistilBERT fine-tuned on the task data, and (ii) a lightweight neural baseline based on a bidirectional LSTM trained either from scratch or initialized with GloVe embeddings. Input representations combine a candidate sense definition with the narrative context and target sentence, separated by a special token. On the official test set, the DistilBERT model achieves the strongest result among our submissions, with an Acc@SD score of 0.54 and Spearman correlation of 0.17, while the best BiLSTM submission reaches 0.52 Acc@SD and 0.02 Spearman correlation. Although DistilBERT performs best in our experiments, the recurrent baseline remains competitive under the tolerance-based metric. We discuss model variants, reproducibility details, and limitations of our analysis.