The article discusses Named Entity Recognition (NER) in recipes and how they can be treated as data. It introduces TASTEset, a dataset of 700 recipe ingredient lists annotated with named entities from nine classes. The author then explains the process of training a BERT-powered NER model for recipes using this dataset. They also discuss the results and inference of the trained model. The article emphasizes that even though the TASTEset code isn't entirely plug-and-play, it is still a valuable resource for those interested in parsing recipes en masse.