This paper presents the PTUK-NLP submission to KnowledgeGraphEval 2026 Subtask 1 (AdaptNER), evaluating cross-domain and cross-dialect nested Arabic Named Entity Recognition (NER). Models trained on Wojood are evaluated in a zero-shot setting on Konooz, spanning ten unseen domains and sixteen Arabic dialects. Our system fine-tunes ArBERT with targeted augmentation for rare entity types and lightweight inference-time post-processing, using Konooz strictly for evaluation and never for training or adaptation. The submitted system achieves 91.07% micro-F1 and 83.11% macro-F1 on Wojood, and 71.84% precision, 66.59% recall, 69.11% micro-F1, and 60.70% macro-F1 on the official Konooz blind test, ranking fourth on the final leaderboard and achieving a micro-F1 score 6.11 points above the organizers’ baseline. A post-submission ensemble of ArBERT and AraBERTv2 does not improve over the best single model. Overall, targeted data-centric modifications provide useful gains for rare entity types, while a standard MSA-pretrained encoder remains competitive under substantial domain and dialect shift.