Multimodal Named Entity Recognition (MNER) uses supplementary images to resolve ambiguity in entity mentions in noisy, short-form social-media text. Prevailing approaches typically fuse text with visually aligned features, but such cues often lack sufficient discriminative power and may support conflicting entity-type hypotheses. To address this issue, we propose TCHV, a lightweight framework grounded in a type-conditioned hypothesis-verification perspective. By formulating each span-type pair as a distinct hypothesis during inference, TCHV predicts the latent feature evidence expected under each hypothesis and validates it against observed multimodal representations through bidirectional text-image matching, using a visual relevance gate to suppress unin- formative images. Verification scores serve as surrogate evidence statistics; TCHV improves over established non-VLLM multimodal baselines on Twitter-2015/2017, and TCHV† leads the compared VLLM-based methods on Twitter-2017. Extensions demonstrate applicability to grounded and fine-grained settings. Code: https://github.com/ hanfeiyu703/ImaginE.git.