Relation extraction (RE) identifies semantic relations between entities, converting unstructured text into structured facts for downstream applications such as knowledge graph construction. Arabic RE remains challenging due to rich morphology, complex syntax, dialectal variation, and the scarcity of high-quality annotated resources. We present Yafa’s Team submission (ranked 7th) to Subtask 2 of the KnowledgeGraphEval 2026 Shared Task, which frames Arabic RE as multiclass classification over 40 relation types from the dataset. We propose an ArBERTv2-based entity-marker classifier that fuses global sentence context ([CLS]) with entity-specific representations (h_e1, h_e2) into a joint representation for relation prediction. Building on this baseline, we exploit a transductive self-training step that pseudo-labels the released test inputs at a softmax confidence threshold of 0.95 and retrains on them, raising Micro-F1 from 70.20% to 91.92%. We further conduct a systematic analysis of cross-backbone ensembling with MARBERTv2 and AraBERTv2, finding that ensembling does not outperform the single transductive model. Our results highlight transductive self-training as a simple yet effective strategy for improving Arabic RE robustness.