  Large language models like GPT have proven widely successful on natural
language understanding tasks based on written text documents. In this paper, we
investigate an LLM's performance on recordings of a group oral communication
task in which utterances are often truncated or not well-formed. We propose a
new group task experiment involving a puzzle with several milestones that can
be achieved in any order. We investigate methods for processing transcripts to
detect if, when, and by whom a milestone has been completed. We demonstrate
that iteratively prompting GPT with transcription chunks outperforms semantic
similarity search methods using text embeddings, and further discuss the
quality and randomness of GPT responses under different context window sizes.
