Even seemingly simple drawings, diagrams, and sketches are hard for computer programs to interpret, because these inputs can be highly variable in several respects. This variability corrupts the expected mapping between a prior model of a configuration and an instance of it in the scene. We propose a scheme for representing ambiguity explicitly, within a subgraph matching framework, that limits its impact on the computational and program complexity of matching. First, ambiguous alternative structures in the input are explicitly represented by coupled subgraphs of the data graph, using a class of segmentation post-processing operations termed graph elaboration. Second, the matching process enforces mutual exclusion constraints among these coupled alternatives, and preferences or rankings associated with them enable better matches to be found early on by a constrained optimization process. We describe several elaboration processes, and extend a straightforward constraint-based subgraph matching scheme to elaborated data graphs. The discussion focuses on the domain of human stick figures in diverse poses.