We present the Team Falcons system for the ImageEval 2026 Task 1b English track. The task provides culturally grounded images from the OASIS/EverydayMMQA resource family and three English statements per image, exactly one of which is visually supported(CITATION). Rather than treating the statements as independent binary decisions, we exploit this structural constraint and formulate each item as image-conditioned three-way classification. In Stage 1, we fine-tune Qwen3-VL-8B(CITATION) with 4-bit QLoRA(CITATION) and estimate uncertainty from the first-token probabilities assigned to A, B, and C. Stage 2 we send only examples whose top-two probability margin is below 0.90 to Gemini 3.6 Flash and GPT-5.4-mini. Their answer replaces the Stage 1 prediction only when both verifiers return the same valid option. On the official test set, Stage 1 obtains a Contrastive Instability (CI) of 0.0360 and Combined Accuracy of 0.9640. Consensus verification improves these scores to 0.0290 and 0.9710, respectively, placing our submission first on the Task 1b English leaderboard. After test labels were released, we found that the verifiers agreed on 91 of 138 routed items and changed 17 predictions: 12 corrections and 5 harmful revisions. These results show that structure-aware fine-tuning and conservative uncertainty routing can effectively combine a local open-weight VLM with proprietary multimodal verifiers.