We present RPD-UoF, a corpus of 3,091 police use-of-force narratives obtained via Freedom of Information Law (FOIL) requests from the Rochester Police Department, combining structured demographic metadata with officer-authored free-text accounts. We use this dataset to investigate three related questions: whether officer narratives contain recoverable racial signal after demographic redaction, whether large language models amplify racial framing when conditioned on subject race, and whether that signal survives text redaction to re-emerge in multimodal outputs. Redacted narratives are statistically separable by subject race; LLMs produce structurally longer narratives for White profiles than Black profiles from identical seed texts; and CLIP classifications of GPT-5-generated images differ significantly depending on the racial origin of the source narrative. Together, these analyses provide an initial empirical lens on how race appears in police narratives and how modern language models interact with such sensitive attributes. The dataset and analyses are intended to support future research on fairness, accountability, and transparency in computational studies of policing and criminal justice.