Narrative text embedding is the basis for machines to understand and represent stories. However, it is challenging because it depends on similarities in theme, course of action, and outcomes. To target this challenge, we present a task-aligned system for SemEval-2026 Task 4 Track B. We first use Qwen2.5-32B-Instruct model to generate hard negatives from three narrative dimensions. We then train a Qwen3-Embedding-8B model with a multi-negative contrastive objective and use a teacher model that has the same architecture as the training model. The model achieves the best result in the current training phase by introducing "soft label" via KL Divergence.