  Large Language Models (LLMs) are capable of answering questions in natural
language for various purposes. With recent advancements (such as GPT-4), LLMs
perform at a level comparable to humans for many proficient tasks. The analysis
of business processes could benefit from a natural process querying language
and using the domain knowledge on which LLMs have been trained. However, it is
impossible to provide a complete database or event log as an input prompt due
to size constraints. In this paper, we apply LLMs in the context of process
mining by i) abstracting the information of standard process mining artifacts
and ii) describing the prompting strategies. We implement the proposed
abstraction techniques into pm4py, an open-source process mining library. We
present a case study using available event logs. Starting from different
abstractions and analysis questions, we formulate prompts and evaluate the
quality of the answers.
