Commonsense knowledge bases such as Con-ceptNet represent knowledge in the form of relational triples. Inspired by recent work by We propose novelty of predicted triples with respect to the training set as an important factor in interpreting results. We critically analyse the difficulty of mining novel commonsense knowledge, and show that a simple baseline method outperforms the previous state of the art on predicting more novel triples.