  Starting from the COMET methodology by Bosselut et al. (2019), generating
commonsense knowledge directly from pre-trained language models has recently
received significant attention. Surprisingly, up to now no materialized
resource of commonsense knowledge generated this way is publicly available.
This paper fills this gap, and uses the materialized resources to perform a
detailed analysis of the potential of this approach in terms of precision and
recall. Furthermore, we identify common problem cases, and outline use cases
enabled by materialized resources. We posit that the availability of these
resources is important for the advancement of the field, as it enables an
off-the-shelf-use of the resulting knowledge, as well as further analyses on
its strengths and weaknesses.
