Drug–disease knowledge underpins drug discovery and precision medicine, yet most of it is locked in unstructured PubMed articles and only partially captured by curated databases. We release UniD³-KG, a suite of six knowledge graphs spanning three tasks (Drug–Disease Matching, DDM; Drug Effectiveness Assessment, DEA; and Drug–Target Analysis, DTA), and UniD³-data, three open QA datasets (28,915 DDM, 15,042 DEA and >4,000 DTA pairs), distilled from 157,849 PubMed articles with Llama-3.3-70B by the UniD³ KG-RAG pipeline, which couples task-specific prompting with a dual-stage entity extraction that re-extracts over the consolidated graph to recover cross-chunk and cross-document evidence. External validation on CDR, DrugReviews, P3ps and MedicationQA and a benchmark across seven LLM baselines and three retrieval variants show that UniD³-data is faithful to curated sources and that retrieval over UniD³-KG consistently improves biomedical QA; a clinician verification of 469 FDA-approved, US-marketed drugs further confirms strong agreement with the UniD³-data DDM split (accuracy 0.90, F1 0.90, AUROC 0.90). All artefacts—UniD³-KG, UniD³-data, benchmark scripts and the UniD³-QA explainable chatbot—are released as shared infrastructure for biomedical NLP and drug repurposing.