Movie screenplays are rich long-form narratives that interleave complex character relationships, temporally ordered events, and dialogue-driven interactions. While prior benchmarks target individual subtasks such as question answering or dialogue generation, they rarely evaluate whether models can construct a coherent story world and use it consistently across multiple forms of reasoning and generation. We introduce STAGE (Screenplay Text, Agents, Graphs and Evaluation), a unified benchmark for narrative understanding over full-length movie screenplays. STAGE defines four tasks: knowledge graph construction, scene-level event summarization, long-context screenplay question answering, and in-script character role-playing, all grounded in a shared narrative world representation. The benchmark provides cleaned scripts, curated knowledge graphs, and event- and character-centric annotations for 150 films across English and Chinese, enabling holistic evaluation of models' abilities to build world representations, abstract and verify narrative events, reason over long narratives, and generate character-consistent responses.

翻译：电影剧本是丰富的长篇叙事，交织着复杂的人物关系、时序性事件以及对话驱动的互动。尽管现有基准针对问答或对话生成等独立子任务，但它们很少评估模型能否构建连贯的故事世界，并在多种推理与生成形式中一致地运用该世界。我们提出了STAGE（Screenplay Text, Agents, Graphs and Evaluation），一个面向全长电影剧本的叙事理解统一基准。STAGE定义了四项任务：知识图谱构建、场景级事件摘要、长上下文剧本问答以及剧本内角色扮演，所有任务均基于共享的叙事世界表示。该基准提供了150部涵盖英语和中文电影的清洗后剧本、精校知识图谱，以及以事件和人物为中心的标注，从而能够全面评估模型在构建世界表示、抽象与验证叙事事件、长叙事推理以及生成角色一致响应方面的能力。