Uncertainty annotations such as "confidence = 0.7" are increasingly prevalent in semantic infrastructures for
Cultural Heritage and Digital Humanities, where they render uncertainty machine-readable and support ranking,
filtering, and aggregation processes. In collection and provenance research, however, attributions frequently
rest on fragmentary evidence, competing interpretations, and ongoing scholarly debates, such that numerical
precision without an explicit reference frame remains semantically underdetermined. This paper investigates
how uncertainty in RDF-based knowledge graph infrastructures can be modelled and published in ways that
make it not only machine-readable but also interpretable, revisable, and responsibly reusable. The guiding thesis
is that uncertainty is not a property of an object but a feature of a research judgment. We formulate a typology
of uncertainty forms relevant to DH and CH research and show why each resists scalar representation. To
ground requirements in practice, we report insights from an exploratory workshop with 15 professionals from
Galleries, Libraries, Archives, and Museums (GLAM), analysed using Reflexive Thematic Analysis in light of
an Human-Computer Interaction-informed perspective on interpretability, from which recurring requirements
emerge concerning transparent uncertainty marking, authorship, temporality, revisability, and the visibility
of alternatives. On this basis, we propose a lightweight modelling pattern that represents uncertainty as an
attributed, contextualised, and referenceable evaluation of a claim. We illustrate it with a case of conflicting
narratives from Sammlung Kulturen der Welt in Lübeck, where at least five epistemically incompatible claims
coexist, a complexity no scalar value can capture.