We propose GOTPR, a robust place recognition method designed for outdoor environments where GPS signals are unavailable. Unlike existing approaches that use point cloud maps, which are large and difficult to store, GOTPR leverages scene graphs generated from text descriptions and maps for place recognition. This method improves scalability by replacing point clouds with compact data structures, allowing robots to efficiently store and utilize extensive map data. In addition, GOTPR eliminates the need for custom map creation by using publicly available OpenStreetMap data, which provides global spatial information. We evaluated its performance using the KITTI360Pose dataset with corresponding OpenStreetMap data, comparing it to existing point cloud-based place recognition methods. The results show that GOTPR achieves comparable accuracy while significantly reducing storage requirements. In city-scale tests, it completed processing within a few seconds, making it highly practical for real-world robotics applications. More information can be found at https://donghwijung.github.io/GOTPR_page/.