  Link Prediction(LP) is an essential task over Knowledge Graphs(KGs),
traditionally focussed on using and predicting the relations between entities.
Textual entity descriptions have already been shown to be valuable, but models
that incorporate numerical literals have shown minor improvements on existing
benchmark datasets. It is unclear whether a model is actually better in using
numerical literals, or better capable of utilizing the graph structure. This
raises doubts about the effectiveness of these methods and about the
suitability of the existing benchmark datasets.
  We propose a methodology to evaluate LP models that incorporate numerical
literals. We propose i) a new synthetic dataset to better understand how well
these models use numerical literals and ii) dataset ablations strategies to
investigate potential difficulties with the existing datasets. We identify a
prevalent trend: many models underutilize literal information and potentially
rely on additional parameters for performance gains. Our investigation
highlights the need for more extensive evaluations when releasing new models
and datasets.
