The efficacy of neural “retrieve and generate” systems is well established for question answering (QA) over unstructured text. Recent efforts seek to extend this approach to knowledge graph (KG) QA by converting structured triples to unstructured text. However, the relevance of KG triples retrieved by these systems limits their accuracy. In this paper, we improve the relevance of retrieved triples using a carefully designed re-ranker. Specifically, our pipeline (i) retrieves over documents of triples grouped by entity, (ii) re-ranks triples from these documents with context: triples in the 1-hop neighborhood of the documents’ subject entity, and (iii) generates an answer from highly relevant re-ranked triples. To train our re-ranker, we propose a novel “triple-level” labeling strategy that infers fine-grained labels and shows that these significantly improve the relevance of retrieved information. We show that the resulting “retrieve, re-rank, and generate” pipeline significantly improves upon prior KGQA systems, achieving a new state-of-the-art on FreebaseQA by 5.56% Exact Match. We perform multiple ablations that reveal the distinct benefits of our contextual re-ranker and labeling strategy and conclude with a case study that highlights opportunities for future works.