Dream reports are recollections of our experiences while asleep, and have strong research and clinical value. Since their analysis can be extremely time-consuming, researchers have adopted multiple types of automatised approaches, including, in more recent years, pre-trained language models (PLMs). However, most work has focused on limited aspects of the report content, such as characters or emotions. In this work, we introduce PreDA (prefix-based dream reports annotation), a framework to build language models able to annotate a dream report for multiple relevant aspects, using generative PLMs. We provide experimental evidence showing how a single PLM of small dimension can efficiently annotate a report on multiple features of the Hall and Van De Castle (HVDC) framework, give a detailed analysis of the model’s performance, and explain how the training data impact learning and generalisation ability of the model.