We used <hi xmlns="http://www.tei-c.org/ns/1.0" rend="color(#0e101a)">GPT-4o mini </hi>and LLaVA to automatically annotate the documentary. First, we extracted one frame per second from the documentary using the OpenCV library for Python. Next, we evaluated 20 different prompt variations for image annotation using a random sample of 30 video frames. Through manual evaluation of descriptive accuracy and contextual completeness, we created two optimal prompts for each model. These prompts can be considered zero-shot prompts (Kojima et al., 2022), which provide no contextual information, as we wanted the LLM to ‘interpret’ as little as possible and instead describe the visual images in general terms: