Large language models (LLMs) excel at operating at scale by leveraging social media and data crawled from the web. Whereas existing corpora are diverse, their frequent lack of long-term temporal structure may however limit an LLM's ability to contextualize semantic and normative evolution of language and to capture diachronic variation. To support the latter, we introduce Chronoberg, a temporally structured corpus of English book texts spanning 250 years, curated from Project Gutenberg and enriched with a variety of temporal annotations. First, the edited nature of books enables us to quantify lexical semantic change through time-sensitive Valence-Arousal-Dominance analysis and to support historically-grounded interpretation. With the lexicons at hand, we demonstrate a need for modern LLM-based tools to better situate their contextualization of sentiment and detection of discriminatory language across various time-periods. In fact, we show how these tools struggle with diachronic shifts in meaning, emphasizing the need for temporally aware training and evaluation pipelines, and positioning Chronoberg as a scalable resource for respective studies. Disclaimer: This paper includes language and display of samples that could be offensive to readers. Open Access: Chronoberg is available publicly on HuggingFace at https://huggingface.co/datasets/spaul25/Chronoberg. Code is available at https://github.com/paulsubarna/Chronoberg.

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