Data for Human Review

Many interoperability, knowledge graph, metadata harmonisation, and AI-assisted data processing workflows ultimately depend on human judgement. Automated systems can often narrow ambiguity, identify candidate correspondences, and generate plausible semantic assertions, but they frequently cannot determine with sufficient confidence whether a claim should be corroborated, rejected, or referred for further review. This paper introduces `Betwixt`, a lightweight model for representing and reviewing semantic claims. `Betwixt` reduces the cognitive burden of semantic review by allowing claims to be assessed at the most appropriate level of abstraction. Its pragmatic approach represents a claim as a scoped assertion consisting of four components: scope, subject, predicate, and value. The model is intentionally simple. Rather than defining a new ontology, graph format, or review platform, it provides a minimal semantic representation that can be rendered through existing templating systems and reviewed through existing user interfaces. At the core of the model is a provenance-preserving review pipeline in which observations give rise to candidate claims, candidate claims are subjected to human review, and review outcomes create reviewed claims together with an explicit stabilisation history. In this way, `Betwixt` separates the provenance of observations from the provenance of review while maintaining both as first-class elements of the semantic workflow. The objective is to provide a common review layer between observation and semantic object. `Betwixt` is designed to support semantic stabilisation by making candidate claims explicit, reviewable, and reproducible while remaining independent of any specific software environment.