  Over the past decade, extensive research efforts have been dedicated to the
extraction of information from textual process descriptions. Despite the
remarkable progress witnessed in natural language processing (NLP), information
extraction within the Business Process Management domain remains predominantly
reliant on rule-based systems and machine learning methodologies. Data scarcity
has so far prevented the successful application of deep learning techniques.
However, the rapid progress in generative large language models (LLMs) makes it
possible to solve many NLP tasks with very high quality without the need for
extensive data. Therefore, we systematically investigate the potential of LLMs
for extracting information from textual process descriptions, targeting the
detection of process elements such as activities and actors, and relations
between them. Using a heuristic algorithm, we demonstrate the suitability of
the extracted information for process model generation. Based on a novel
prompting strategy, we show that LLMs are able to outperform state-of-the-art
machine learning approaches with absolute performance improvements of up to 8\%
$F_1$ score across three different datasets. We evaluate our prompting strategy
on eight different LLMs, showing it is universally applicable, while also
analyzing the impact of certain prompt parts on extraction quality. The number
of example texts, the specificity of definitions, and the rigour of format
instructions are identified as key for improving the accuracy of extracted
information. Our code, prompts, and data are publicly available.
