  The task of identifying and reasoning with circumstantial preconditions
associated with everyday facts is natural to humans. It is unclear whether
state-of-the-art language models (LMs) understand the implicit preconditions
that enable or invalidate commonsense facts, such as "A glass is used for
drinking water", Despite their impressive accuracy on existing commonsense
tasks. In this paper, we propose a new problem of reasoning with circumstantial
preconditions, and present a dataset, called CoreQuisite, which annotates
commonsense facts with preconditions expressed in natural language. Based on
this resource, we create three canonical evaluation tasks and use them to
examine the capability of existing LMs to understand situational
pre-conditions. Our results show that there is a 10-30%gap between machine and
human performance on our tasks. We make all resources and software publicly
available.
