  Decoding the core of procedural texts, exemplified by cooking recipes, is
crucial for intelligent reasoning and instruction automation. Procedural texts
can be comprehensively defined as a sequential chain of steps to accomplish a
task employing resources. From a cooking perspective, these instructions can be
interpreted as a series of modifications to a food preparation, which initially
comprises a set of ingredients. These changes involve transformations of
comestible resources. For a model to effectively reason about cooking recipes,
it must accurately discern and understand the inputs and outputs of
intermediate steps within the recipe. Aiming to address this, we present a new
corpus of cooking recipes enriched with descriptions of intermediate steps of
the recipes that explicate the input and output for each step. We discuss the
data collection process, investigate and provide baseline models based on T5
and GPT-3.5. This work presents a challenging task and insight into commonsense
reasoning and procedural text generation.
