For SemEval-2026 Task 4, we introduce a unified two-stage framework based on a RoBERTa-large encoder. Stage 1 performs contrastive pre-training on synthetic triplets to learn general narrative similarity patterns. Stage 2 fine-tunes the model with a ranking-based objective tailored to Track A.The resulting encoder supports both binary similarity classification (Track A) and narrative embedding generation (Track B) without architectural changes. Our system achieves an accuracy of 0.64 on Track A and 0.69 on Track B, outperforming single-stage baselines and demonstrating that combining synthetic contrastive supervision with task-specific ranking yields stable and reusable narrative representations.