Such state-of-the-art entity linking models <cite>(Ganea and Hofmann 2017</cite>; Le and Titov 2018) employ attention-based bag-of-words context model and pre-trained entity embeddings bootstrapped from word embeddings to assess topic level context compatibility. However, the latent entity type information in the immediate context of the mention is neglected. We suspect this may sometimes cause the models link mentions to incorrect entities with incorrect type. To verify this, we conduct error analysis of the well known <cite>DeepED 1</cite> model <cite>(Ganea and Hofmann 2017)</cite> on the development set of AIDA-CoNLL (Hoffart et al. 2011) , and found that more than half of <cite>their</cite> error cases fall into the category of type errors where the predicted entity's type is different from the golden entity's type, although some predictive contextual cue for them can be found in <cite>their</cite> local context.