One of the most widely used elements in misinformation campaigns is media framing via certain angles which in turn implies pitching news stories through a certain narrative. Narrative twisting to align with a political agenda includes complex dynamics involving different topics, patterns and rhetoric; there is however a certain coherence with respect to the media framing agenda that is to be promoted. The shared task’s objective is to develop models for classifying narratives in online news from a pre-defined two-level taxonomy (Subtask 2). In this paper, we discuss the application of a Mistral 7B model, specifically E5 model, to address theSubtask two in English about finding the narrative taxonomy that a news article is trying to pitch. Our approach frames the task as a retrieval task in a similarity matching framework instead of reliance supervised learning. Our approach based on the use of a Mistral 7B model obtains a F1 on samples of 0.226 and is able to outperform the baseline provided for the competition.