Transdisciplinary innovation ecosystems are often organized through time-bound, multi-actor collaborations that operate without stable hierarchy structures commonly conceptualized as heterarchies. To date, efforts to recover relational knowledge in these transient heterarchies and to learn from the practices of historical projects remain limited. Existing tools for knowledge graphing and ecosystem mapping offer limited support for reconstructing relational data from these collaborations once actors disperse. Through our study, we demonstrate how a large language model (LLM) can assist in reconstructing the social network between actors by filling gaps left by actor mobility, organizational churn, and prior collaboration histories. We demonstrate this method through a single case study of Project Arrow, an all-Canadian electric vehicle concept project intended to mobilize national innovation capacity, and a transdisciplinary business heterarchical collaboration involving actors across industry, academia, and public-sector institutions. This paper contributes a methodological approach for retrospective relational knowledge reconstruction in transient innovation ecosystems and shows how LLM-supported prompt engineering can support the development of knowledge maps of heterarchical collaboration.