Definition modeling is the task to generate a valid definition for a given input term. This relatively novel task has been approached either with no context (i.e., given a word embedding alone) and, more recently, as word-in-context modeling. Despite their success, most works make little to no distinction between resources and their specific features (e.g., type and style of definitions, or quality of examples) when used for training. Given the high diversity lexicographic resources exhibit in terms of topic coverage, style and formal structure, it is desirable for downstream definition modeling to better understand which of them are better suited for the task. In this paper, we propose an empirical evaluation of the well-known lexical database WordNet, and specifically, its dictionary examples. We evaluate them both directly, by matching them against criteria for good dictionary writing, and indirectly, in the task of definition modeling. Our results suggest that WordNet’s dictionary examples could be improved by extending them in length, and incorporating prototypicality.