The ANLP_UniSo team submitted a system to Subtask 1 (Named Entity Recognition) of KnowledgeGraphEval 2026, which focuses on nested NER in Arabic and evaluates crossdomain and cross-dialect generalization from the Wojood corpus to the multi-domain Konooz benchmark. The system is a combination of an Efficient GlobalPointer decoder with rotary position embeddings and a MARBERT backbone. To reduce the domain gap, first domainadaptive pretraining (DAPT) is performed on unlabeled Konooz text, and then a pseudolabeling self-training stage is applied. A second model based on AraBERT is trained and combined to the other model using a stacking ensemble to improve prediction diversity. As for the Wojood test set, the best ensemble achieves a micro F1 of 0.9288, increasing to 0.9292 when entity-type-specific decision thresholds are used, compared with 0.9211 for the single MARBERT model. On the official blind Konooz test set, however, the submitted system obtains a micro F1 of 0.7106 (macro F1 = 0.6101, weighted F1 = 0.7013, P = 0.8143, R = 0.6304, accuracy = 0.9948). The lower recall suggests that the decoding threshold chosen based on the Wojood validation data was too strict for the domain-shifted Konooz data. the system study the difference and propose a threshold-recalibration strategy to improve the performance under the target-domain distribution.