<p>Ambiguous questions often require clarification before reliable answers can be generated. Existing clarification-based question answering systems typically generate answers directly from clarification interactions, implicitly assuming that the required information has been fully recovered. We argue that clarification should instead be viewed as a process of recovering the latent semantic state necessary for answer generation. To this end, we propose SemRec-SV, a semantic-state-centric framework for ambiguity resolution that explicitly models semantic-state recovery, verification, and refinement. The framework consists of FTGate for ambiguity detection, semantic-state recovery from clarification interactions, StateVerify for assessing the sufficiency of the recovered state, and an adaptive refinement mechanism that performs additional interaction only when the recovered state is deemed insufficient. Experiments on the ClarifyingQA benchmark show that SemRec-SV achieves the highest overall performance among the evaluated methods, outperforming clarification-only baselines and a reproduced CLAM baseline across multiple instruction-tuned language models. Additional analyses demonstrate that semantic-state recovery provides benefits beyond clarification alone, state verification improves answer reliability by identifying incompletely recovered information, and adaptive refinement is particularly effective on insufficient cases. These results suggest that ambiguity resolution benefits from explicitly modeling the recovery, verification, and refinement of semantic information rather than treating clarification as a direct path to answer generation.</p>