This research examines modern methods for automating information verification, specifically focusing on narrative texts containing dated content. We introduce three new techniques—CHECK-S, CHECK-V, and CHECK-U—for analyzing texts, along with a new approach to contrastive learning, “Hierarchical Contrastive Learning,” which has been evaluated in competitive environments. The findings demonstrate significant improvements over traditional methods, confirming the potential of these techniques in enhancing automated information verification for narrative texts.