Automated knowledge graph (KG) construction involves two core components: nested named entity recognition (NER) and relation extraction (RE). In this work, we address cross-domain nested NER alongside RE. We integrate domain adaptation methods within a mixture-of-experts ensemble system for nested NER from Arabic text, evaluated in a cross-domain setting. The system combines multiple Arabic pretrained encoders for token- and span-level classification with domain data augmentation, and integrates their predictions using a vote-based ensemble routing with a complementary XLM-R expert. For RE, we employ an adversarially trained relation classifier backed by auxiliary entity types, NLI reranking, and structural schema rules, treating no_relation as a standard relation label. We additionally study a positive-relation setting that excludes no_relation from both training and inference. Our NER system reaches 0.7438 micro F1 on the hidden test set. For RE, the positive-relation setting achieves 0.9608 micro F1, while the no_relation-aware system achieves 0.9295.