We describe our submission to HalluScoring 2026 for Arabic hallucination detection in general-culture and Islamic-knowledge question answering. Our system adapts the instruction-tuned 12B-parameter Gemma 4 model with QLoRA. Its central idea is to use the Hashtag AI knowledge-graph retrieval system only during offline data construction: we query facts related to development-set failures, create contrastive training examples, and distill them into the model parameters. The submitted model is closed-book and uses no retrieval, search, or external knowledge at evaluation time. On organizer-provided test annotations, it obtains 73.59 macro-F1 on the 400-example Islamic set and 77.56 on the 1,600-example mixed-domain set; answer-option accuracies are 75.25% and 81.13%. Analysis shows strong science performance but lower recall for subtle hallucinations in history, geography, and Islamic knowledge.