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This article argues for the growing importance of quality metadata and the equation of that quality with precision and semantic grounding. Such semantic grounding requires metadata that derives from intentional human intervention as well as mechanistic measurement of content media. In both cases, one chief problem in the automatic generation of semantic metadata is ambiguity leading to the overgeneration of inaccurate annotations. We look at a particular richly annotated image collection to show how context dramatically reduces the problem of ambiguity over this particular corpus. In particular, we consider both the abstract measurement of "contextual ambiguity" over the collection and the application of a particular disambiguation algorithm to synthesized keyword searches across the selection.