SemEval-2026 Task 6 (CLARITY: Unmasking Political Interview) focuses on detecting equivocation and evasion techniques in political interviews. While encoder-only models and Large Language Models (LLMs) individually struggle with this task, we propose a hybrid BERT–LLM framework to leverage their complementary strengths: the discriminative efficiency of fine-tuned encoders and the sophisticated reasoning of LLMs. We benchmarked several long-context architectures—DeBERTa, RooseBERT, and BigBird—finding that a truncated DeBERTa-large provided the most reliable candidates for the LLM. By using DeBERTa’s top-5 predicted labels as constrained options for LLM inference, we significantly improved evasion-level classification. This hybrid approach achieved competitive rankings in the shared task, placing 7th in Subtask 1 and 2nd in Subtask 2.