The rapid advancement of large language models (LLMs) has enabled powerful AI applications, yet hallucinations remain a major challenge to their reliability. As part of Track 2 of the HalluScoring 2026 shared task at ArabicNLP 2026, we present a system for detecting hallucinated answers and selecting the correct answer from six closely related candidates. We adapt the Arabic–English ALLaM-7B-Instruct model using Low-Rank Adaptation (LoRA) with domain-specific supervision from Qur’anic text, Hadith, Islamic law and inheritance, Islamic history, and general knowledge. The system jointly performs hallucination detection and answer selection, achieving 88.9% accuracy and 88.6% macro-F1 for hallucination detection, and 91.1% answer selection accuracy on the 800-instance development set. Error analysis identifies geography and structured-output parsing as the main remaining weaknesses.