This paper presents our system submitted for SemEval 2016 Task 10: Detecting Minimal Semantic Units and their Meanings (DiM-SUM;Schneider, Hovy, et al., 2016). We extend AMALGrAM (Schneider and Smith, 2015) by tapping two additional information sources. The first information source uses a semantic knowledge base (YAGO3; Suchanek et al., 2007) to improve supersense tagging (SST) for named entities. The second information source employs word embeddings (GloVe; Pennington et al., 2014) to capture fine-grained latent semantics and therefore improving the supersense identification for both nouns and verbs. We conduct a detailed evaluation and error analysis for our features and come to the conclusion that both our extensions lead to an improved detection for SST.