We present a system for rating word sense plausibility in ambiguous narrative contexts for SemEval-2026 Task 5. Our approach ensembles three large language models (Llama-3.1 70B, Qwen-2.5 32B, and Gemma-2 27B) using a computationally efficient, uncertainty-aware pipeline. We combine few-shot chain-of-thought prompting with selective self-consistency, which applies stochastic multiple sampling exclusively to items identified as inherently ambiguous. This targeted strategy reduces inference costs by approximately 45% while maintaining robustness in predictions. To correct the systematic bias of LLMs toward extreme ratings, we apply isotonic regression to shift the output distribution toward patterns of human judgment. Our system achieves a Spearman correlation of 0.67 and an accuracy within 0.76 standard deviations, ranking 34th out of 79 participating teams (top 43% without task-specific fine-tuning). Detailed error analysis reveals that while our system performs strongly on clear contexts (ρ = 0.78), current prompting paradigms struggle significantly to model multimodal human disagreement in genuinely ambiguous cases (ρ = 0.58), highlighting an important challenge for future work on subjective semantic tasks.

This paper presents the VerbaNexAi Lab system for SemEval-2025 Task 2: Entity-Aware Machine Translation (EA-MT), focusing on translating named entities from English to Spanish across categories such as musical works, foods, and landmarks. Our approach integrates detailed data preprocessing, enrichment with 240,432 Wikidata entity pairs, and fine-tuning of the MarianMT model to enhance entity translation accuracy. Official results reveal a COMET score of 87.09, indicating high fluency, an M-ETA score of 24.62, highlighting challenges in entity precision, and an Overall Score of 38.38, ranking last among 34 systems. While Wikidata improved translations for common entities like “Águila de San Juan,” our static methodology underperformed compared to dynamic LLM-based approaches.