  Definitions are a fundamental building block in lexicography, linguistics and
computational semantics. In NLP, they have been used for retrofitting word
embeddings or augmenting contextual representations in language models.
However, lexical resources containing definitions exhibit a wide range of
properties, which has implications in the behaviour of models trained and
evaluated on them. In this paper, we introduce 3D- EX , a dataset that aims to
fill this gap by combining well-known English resources into one centralized
knowledge repository in the form of <term, definition, example> triples. 3D- EX
is a unified evaluation framework with carefully pre-computed
train/validation/test splits to prevent memorization. We report experimental
results that suggest that this dataset could be effectively leveraged in
downstream NLP tasks. Code and data are available at
https://github.com/F-Almeman/3D-EX .
