Large language models used for question answering can produce fluent but factually incorrect answers, a problem that is acute in knowledge-sensitive domains such as Islamic religious knowledge. We describe our system for the HalluScoring 2026 shared task (Islamic Knowledge track), which requires jointly detecting hallucinated answers and selecting the correct option among six candidates. We fine-tune Fanar-1-9B-Instruct with LoRA and decompose inference into two independently-prompted steps, judging hallucination before revealing the candidate options, to prevent a textual-similarity shortcut where one option is a near-verbatim restatement of the generated answer. Our system scores 0.7149 (Assisted) and 0.6222 (Blind) on the official hidden test set, ranking 5th of 5 reported teams; a controlled comparison shows this shortcut-resistant design costs roughly 10 points against a single-pass variant on a held-out development subsample (0.7625 vs. 0.8639 Combined Score). We release our code and model to support further work on this trade-off.