A fundamental requirement for the successful application of process mining are event logs of high data quality that can be constructed from structured data stored in organizations’ core information systems. However, a substantial amount of data is processed outside these core systems, particularly in organizations doing consumer business with many customer interactions per day, which generate high amounts of unstructured text data. Although Natural Language Processing (NLP) and machine learning enable the exploitation of text data, these approaches remain challenging due to the required high amount of labeled training data. Recent advances in NLP mitigate this issue by providing pre-trained and ready-to-use language models for various tasks such as Natural Language Inference (NLI). In this paper, we develop an approach that utilizes NLI to derive topics and process activities from customer service conversations and that represents them in a standardized XES event log. To this end, we compute the probability that a sentence describing the topic or the process activity can be inferred from the customer’s inquiry or the agent’s response using NLI. We evaluate our approach utilizing an existing corpus of more than 500,000 customer service conversations of three companies on Twitter. The results show that NLI helps construct event logs of high accuracy for process mining purposes, as our successful application of three different process discovery algorithms confirms.