This technical report describes the intersection of process mining and large language models (LLMs), specifically focusing on the abstraction of traditional and object-centric process mining artifacts into textual format. We introduce and explore various prompting strategies: direct answering, where the large language model directly addresses user queries; multi-prompt answering, which allows the model to incrementally build on the knowledge obtained through a series of prompts; and the generation of database queries, facilitating the validation of hypotheses against the original event log. Our assessment considers two large language models, GPT-4 and Google's Bard, under various contextual scenarios across all prompting strategies. Results indicate that these models exhibit a robust understanding of key process mining abstractions, with notable proficiency in interpreting both declarative and procedural process models. In addition, we find that both models demonstrate strong performance in the object-centric setting, which could significantly propel the advancement of the object-centric process mining discipline. Additionally, these models display a noteworthy capacity to evaluate various concepts of fairness in process mining. This opens the door to more rapid and efficient assessments of the fairness of process mining event logs, which has significant implications for the field. The integration of these large language models into process mining applications may open new avenues for exploration, innovation, and insight generation in the field.