NLI models are trained to determine the relationship between a premise and a hypothesis. Existing approaches either identify a sentence in the reference text as the premise (sentence-level NLI) , or measure entailment using the entire reference as the premise . However, a single sentence may lack sufficient content, and using the entire reference is inefficient for long texts . Context-level NLI addresses this by retrieving relevant sentences to create a short context for NLI , although it still does not operate at a sufficiently detailed or precise level of granularity . Here, we propose a novel atomic-level NLI evaluation for the chemical domain to tackle such limitations.