  This paper investigates models of event implications. Specifically, how well
models predict entity state-changes, by targeting their understanding of
physical attributes. Nominally, Large Language models (LLM) have been exposed
to procedural knowledge about how objects interact, yet our benchmarking shows
they fail to reason about the world. Conversely, we also demonstrate that
existing approaches often misrepresent the surprising abilities of LLMs via
improper task encodings and that proper model prompting can dramatically
improve performance of reported baseline results across multiple tasks. In
particular, our results indicate that our prompting technique is especially
useful for unseen attributes (out-of-domain) or when only limited data is
available.
