KG-Aided Q&A Accuracy

Accurate answers because the harness is driven by a knowledge graph.

KG curated by screencast-recorder, kg-generator, and GPT-5 Chat Codex on behalf of Kingsley Uyi Idehen.

This HTML document is about the Q&A accuracy shown in the session: the user asks natural-language questions, the harness uses RDF KG-backed context, and the answer returns with resolver-linked provenance.

Actual Session Screenshots Used

These are the only screenshots used in the screencast.

Actual Q&A screenshot: coding agent harness selection criteria
Harness selection criteria: HowTo-backed answer with resolver-linked KG provenance. Source: How to choose a coding agent harness.
Actual Q&A screenshot: Pi OpenCode Claude Code and Codex comparison
Coding harness comparison: product distinctions returned from the UB-backed graph. Source: How do Pi, OpenCode, Claude Code, and Codex differ?.
Actual Q&A screenshot: Britain transformative inventions
Britain invention pattern: cross-domain answer with linked provenance. Source: What historical pattern does Britain have with breakthrough inventions?.

What This Demonstrates

A progressively enriched RDF knowledge base, deployed as a Semantic Web using Linked Data principles, gives the harness a deterministic route from question to answer. It improves on Andrej Karpathy's markdown-based LLM wiki idea by making notes queryable, link-resolvable, and source-grounded. Resolver links make the evidence inspectable, so the answer is not just plausible prose; it is connected to named source entities and relationships.