Previous work (Yamada et al. 2016 ; <cite>Ganea and Hofmann 2017)</cite> on learning entity representation are mostly extensions of the embedding methods proposed by (Mikolov et al. 2013 ). An entity's context is a bag-ofwords representation which mainly captures topic level entity relatedness rather than entity type relatedness. In contrast, we propose a simple method to build entity embeddings directly from pre-trained BERT (Devlin et al. 2019) which can better capture entity type information.