This paper presents a multistage ensemble-based approach developed for the HalluScoring-2026 shared task on Arabic hallucination detection. The proposed system combines three Arabic pretrained language encoders: MARBERTv2, CAMeLBERT, and ARBERT. The diverse linguistic representations captured by these models enable the ensemble to improve the robustness and discriminative capability of hallucination detection compared to the individual encoders. On the development set, the ensemble achieved an AUC-ROC of 0.9413, Macro-F1 of 0.8704, and an AUC-PR of 0.9610, outperforming the individual models. On the official test set, the system achieved an AUC-ROC of 0.7717, Macro-F1 of 0.6576, and an AUC-PR of 0.9181. These results secured the highest overall ranking among all submitted systems, placing our system first in the HalluScoring-2026 shared task. Further analysis reveals substantial variation in detection performance across generator models and linguistic phenomena, with subtle factual deviations, dialectal and culturally specific expressions, and unusually long responses posing particular challenges. Overall, the findings demonstrate the effectiveness of combining distinct Arabic-specific pretrained encoders for robust Arabic hallucination detection.