This paper presents the ArmyForNER system for Subtask 1 of the KnowledgeGraphEval 2026 shared task on Arabic named entity recognition (NER). We address nested, cross-domain Arabic NER with a multi-stage pipeline built on ARBERTv2: (1) domain-adaptive pre-training (DAPT) via masked language modelling on unlabelled Konooz text, (2) fine-tuning with a multi-head BIO tagging architecture on the Wojood nested NER corpus, and (3) self-training on pseudo-labels produced by an intermediate DAPT ensemble over the Konooz domains. Soft-voting three self-trained checkpoints obtains 74.85 micro-F1 and 65.20 macro-F1 on the official test set, against a 63.00 / 44.00 base- line. Because both adaptation stages consume the blind Konooz test sentences, our system is listed among those that used Konooz during training, and its score is not comparable to systems that used Konooz for evaluation only. We state this setting explicitly and find that self-training gives consistent gains across seeds, whereas the benefit of DAPT is configuration- and seed-dependent.

In this paper, we present a relation extraction system developed for Subtask 2 of KnowledgeGraphEval 2026. The system fine-tunes a single ARBERTv2 encoder on inputs with typed entity markers that expose entity boundaries and types, also classifies each entity pair from the encoder’s classification token ([CLS]) state concatenated with the states at the subject and object start markers. It is trained over the full 41-label space, including no-relation, with focal loss, class-balanced sampling, label smoothing, and no-relation down-weighting to counter severe class imbalance. Our submission ranked third of seven teams with a micro-F1 of 0.9332, 0.23 points above the official baseline, which fine-tunes the same encoder. We also report an analysis of seed-dependent behaviour on fine-grained geopolitical relations from our earlier development experiments and discuss the limitations of our evaluation.