Large Language Models are rapidly evolving in the field of Natural Language Processing as well as Vision and Language. In particular, GPT-4o integrates images and text, and is capable of effective Visual Question Answering. However, GPT-4o is limited in its ability to accurately detect the quantity (count or volume), position, and size of objects in a given image, which hinders its practical application to industrial Anomaly Detection (AD). In order to improve the accuracy and interpretability of AD, this study proposes a new pipeline that utilizes GPT-4o to improve both logical and structural AD accuracies and to output natural language explanations of the detected anomalies. Our system first performs a first-pass logical AD by leveraging the results of the MM-Grounding-DINO object detection model and the SAM2 object segmentation model. It then constructs a multimodal prompt in order to make GPT-4o perform AD with accompanied by natural language explanations of the nature of the anomalies. Experimental results with the MVTec LOCO AD dataset show that our system outperforms existing models in a logical AD task, although it performs less well in a structural AD task. Moreover, to the best of our knowledge, our system is the first to achieve explainable AD that can handle both structural and logical anomalies.