This paper describes the approach used to address the task of narrative classification, which has been proposed as a subtask of Task 10 on Multilingual Characterization and Extraction of Narratives from Online News at the SemEval 2025 campaign. The task consists precisely in assigning all relevant sub-narrative labels from a two-level taxonomy to a given news article in multiple languages (i.e., Bulgarian, English, Hindi, Portuguese and Russian). This involves performing both multi-label and multi-class classification. The model developed for this purpose uses multiple pretrained BERT-based models to create contextualized embeddings that are concatenated and then fed into a simple neural network to compute classification probabilities. Results on the official test set, evaluated using samples $F_1$, range from $0.15$ in Hindi (rank #9) to $0.41$ in Russian (rank #3). Besides an overview of the system and the results obtained in the task, the paper also includes some additional experiments carried out after the evaluation phase along with a brief discussion of the observed errors.