按需经济的快速发展深刻重塑了城市交通与物流格局，但将多站点配送任务序列与实际路径几何信息关联的公开数据仍十分稀缺。本文发布了一个基于饿了么平台脱敏记录构建的北京市即时配送任务城市尺度路径重建路线数据集。该数据集包含2020年2月期间79,648条重建的配送波次记录，关联986个匿名骑手标识符，覆盖267,529单订单。每条配送波次中，“派单”“取餐”和“送达”动作点均按原始记录时间戳排序，并通过高德地图骑行路径服务查询相邻动作点之间的路径，从而生成连续的路径几何信息。最终数据集记录了基于导航的骑行路径及对应的动作序列，揭示出具有启发性的时空规律性。原始记录验证表明，路径距离具有高度一致性，而耗时具有一致性中等水平。该城市数据资源可支持城市分析与交通管理领域的研究者开展配送活动研究、建模城市物流系统，并制定可持续发展政策。

The rapid expansion of the on-demand economy has profoundly reshaped urban mobility and logistics, yet open data linking multi-stop delivery task sequences with route geometry remain scarce. Here, we present a city-scale, path-reconstructed route dataset for instant-delivery tasks in Beijing, built from desensitized platform records from Ele.me. The dataset contains 79,648 reconstructed delivery-wave records associated with 986 anonymized courier identifiers and covering 267,529 orders during February 2020. For each wave, Assign, Pickup, and Delivery action points are ordered by source-record timestamps, and Amap cycling routes are queried between consecutive action points to create continuous route geometries. The resulting dataset records navigation-based riding paths and the associated action sequence for each delivery wave, revealing insightful spatiotemporal regularities. Source-record validation shows strong agreement for distance and moderate agreement for duration. This urban data resource enables researchers in urban analytics and transportation management to investigate delivery activities, model urban logistics systems, and develop sustainable policies.