Extracting Named Entity Recognition (NER) associated with signs and symptoms, diseases and syndromes, or health care activities from raw text in maternal electronic health records (EHR) can help in the management of high maternal risk. In addition, identify if the entity is present, absent, or is only a possibility is important to avoid misinterpretation. In this paper, we present an implementation of an automatic information extraction scheme from text in EHR oriented to detect entities and assertions in maternal electronic health records using GPT-3.5 turbo through a few shot architecture. Additionally, we developed a prompt-based approach to interact with the model and obtain NER responses in JSON format, enabling integration with existing healthcare systems and workflows. Our results show an F1-score of 0.75 for NER and 0.76 for assertions. These results show that large languaje models can be used to extract information from EHRs which can improve patient follow-up.