  We tackle \ac{NED} by comparing entities in short sentences with \wikidata{}
graphs. Creating a context vector from graphs through deep learning is a
challenging problem that has never been applied to \ac{NED}. Our main
contribution is to present an experimental study of recent neural techniques,
as well as a discussion about which graph features are most important for the
disambiguation task. In addition, a new dataset (\wikidatadisamb{}) is created
to allow a clean and scalable evaluation of \ac{NED} with \wikidata{} entries,
and to be used as a reference in future research. In the end our results show
that a \ac{Bi-LSTM} encoding of the graph triplets performs best, improving
upon the baseline models and scoring an \rm{F1} value of $91.6\%$ on the
\wikidatadisamb{} test set
