Robots navigating open-world, human-centered environments must look beyond prebuilt high-fidelity maps and leverage navigational aids embedded in their surroundings, particularly, signs. To advance sign-guided visual navigation, we introduce SiGNgapore, an interactive, multimodal dataset designed to benchmark sign-guided multi-step navigational decision-making. SiGNgapore covers diverse public spaces in Singapore, including shopping malls, public transportation hubs, and hospitals, featuring multi-floor, multi-building layouts and indoor-outdoor transitions. It consists of 56 long-horizon navigation missions and 456 scenes, each containing a navigational sign and its surrounding environment. We provide synchronized RGB, depth, IMU, and odometry data for each scene. We additionally provide the layout map and the GPS-aligned scene graph for selected mission sites. Beyond navigational decision-making, SiGNgapore supports a variety of spatial-semantic reasoning tasks, including navigational sign parsing and visual sign grounding, with the ultimate aim of driving progress in mapless robot navigation.

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