Benchmarking Geographical Temporal Knowledge Graph Reasoning

Temporal knowledge graphs (TKGs) built from event data carry rich latent geography, yet existing TKG question answering (TKGQA) benchmarks test only structural and temporal reasoning. We present GeoTKGQA, a question-answer generator whose questions provably require geographical reasoning by construction. We ground the locative anchors of a TKG to a shared geographical knowledge layer and design twelve schema families that compose structural, temporal, and spatial operators. We then execute every logical form symbolically and keep a question only if dropping the constraints of any one axis would change its gold answer. Applied to four standard event graphs, the pipeline yields 67,565 tri-axially entangled questions with difficulty bands and four generalization splits, without human annotation or large language models. We evaluate ten baselines from four paradigms and analyze all 160 runs with a generator-backed error attribution protocol. The strongest system wins ten out of sixteen benchmark cells, but resolves only 0.4116 of mean-pooled questions, while 19.94% of the questions defeat all ten methods. GeoTKGQA's code and data are anonymously available at https://anonymous.4open.science/r/GeoTKGQA-6FB8/.