Meetings are the keystone of a good company. They allow for quick decision making, multiple-perspective problem solving and effective communication. However, most employees and managers have a negative view on the efficiency and quality of their meetings. High quality meetings where every participant feels equally heard and respected is crucial for having positive meeting sentiment within a company. One of the most influential aspects of meetings are speech overlaps. Overlaps range from short utterances such as backchannels, to follow up questions and clarifications, to complete interruptions. In non-competitive cases, the overlapped speaker feels that the other participants are listening and actively engaging with them during the meeting. In competitive cases, the overlapped speaker can feel interrupted and unimportant. Therefore, competitive overlaps often have a negative impact on the course of the discussion and the overlappee's meeting sentiment. In problematic cases, these overlaps should be reduced to a minimum. In order to do this, overlaps must be classified as either competitive or non-competitive. This paper proposes a novel approach to overlap classification, namely that of text-based classification through Large Language Models. Four different prompt designs are used and tested on the two best performing and publicly available models, GPT-3.5-turbo and GPT-4. The results show that the in-context learning approach using the GPT-4 model results in the most accurate classifications. When comparing the results to previous work, it is observed that the text-based GPT-4 model matches carefully engineered neural networks that even adopt a multi-modular approach.