Mitigating factual hallucinations in Large Language Model (LLM) responses is critical, especially when handling sacred Islamic text. This paper presents our system for the IslamicEval 2026 Shared Task-Subtask 1 (Span Detection), which requires extracting exact character-level spans and fragment labeling of religious citations in Arabic LLM-generated responses. We frame this as a supervised token-classification task, fine-tuning a monolingual bidirectional encoder (AraBERTv2). To process long sequences, we implement a sliding window chunking mechanism with an overlapping stride. We reconstruct final character boundaries using a post-processing heuristic that resolves overlapping token predictions. Furthermore, we apply a cascading false-positive filter using cross-type conflict resolution and category-specific minimum length thresholds. Our pipeline yields highly competitive results, achieving a Macro-F1 score of 0.9652 on the development set and a peak evaluation score of 0.9736 on the official-task test set.