Relation extraction (RE) is a fundamental component of knowledge graph construction, question answering, and text summarization. Although recent RE models achieve strong overall performance through the fine-tuning of pretrained language models, their aggregate performance can obscure substantial disparities across relation types, particularly under long-tailed distributions. This issue remains largely unexplored for Arabic RE. In this work, we investigate the effectiveness of class-balancing strategies for long-tail relation extraction in Arabic by fine-tuning AraBERT with entity markers on Wojood^Relations. Our system, submitted to the KnowledgeGraphEval 2026 shared task, achieves a Micro-F1 score of 92.73 on the official test set. Further analysis shows that errors are disproportionately concentrated among low-frequency and semantically overlapping relation types, indicating that imbalance-handling strategies developed primarily for English benchmarks do not fully address the challenges posed by the long-tailed relation distribution in Arabic.