Hallucination detection in Arabic Islamic question answering is challenging because fluent answers may misstate a verse, narration, ruling, attribution, or historical fact. The task jointly requires detecting hallucination and recovering the factually correct option. We introduce C3R, a Counterfactual Cross-Check Recognition framework that models this relation with two role-conditioned predictions from one Arabic language model: a truth role selects the factual option, while a match role selects the candidate that best represents the generated response. Training uses targeted counterfactual responses, hard distractors, option-order permutations, and completion-likelihood supervision without a cross-view consistency loss. At inference, C3R scores valid structured completions, maps predictions from permuted presentations back to semantic option identities, and derives the hallucination label from argmax agreement between the two roles. The cross-check is applied only at inference. With a QLoRA-adapted ALLaM backbone, C3R ranks first on the official test leaderboard with a final score of 0.8824, comprising 0.8673 macro-F1 and 0.8975 option accuracy. These results demonstrate the effectiveness of structured cross-role agreement for knowledge-sensitive hallucination detection and answer verification.