  We develop Process Execution Graphs (PEG), a document-level representation of
real-world wet lab biochemistry protocols, addressing challenges such as
cross-sentence relations, long-range coreference, grounding, and implicit
arguments. We manually annotate PEGs in a corpus of complex lab protocols with
a novel interactive textual simulator that keeps track of entity traits and
semantic constraints during annotation. We use this data to develop
graph-prediction models, finding them to be good at entity identification and
local relation extraction, while our corpus facilitates further exploration of
challenging long-range relations.
