Knowledge graph (KG) foundation models aim to generalize to graphs with unseen entities and relations by learning transferable relational structure. Most existing methods, however, focus on relation-level universality, leaving in-context learning, the other pillar of foundation models, largely unexplored for KG reasoning. Context in KGs is structured and heterogeneous: accurate prediction requires conditioning both on the local neighborhood of the query entities and on global context that summarizes how the query relation behaves across many instances. We propose KGPFN, a KG foundation model built on a Prior-Data Fitted Network (PFN) that combines transferable relational representations with inference-time in-context learning over structured context. KGPFN learns relation representations by message passing on relation graphs and extracts multi-scale local context from the intermediate head representations of a multi-layer NBFNet. It then builds relation-specific global context from positive and negative examples of the query relation, together with their local structural representations, and aggregates this context with feature-level and sample-level attention. Through multi-graph pretraining, KGPFN learns to combine structural representations with labeled contextual evidence without inference-time parameter updates. On 57 knowledge graphs, KGPFN achieves the best average MRR both without and with fine-tuning, and context sensitivity analyses highlight the value of negative context examples. Our code is available at https://github.com/HKUST-KnowComp/KGPFN.

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