The development of fact-checking systems for verifying the factuality of text generated by large language models (LLMs) has been advancing.In the verdict prediction step of such systems, the system determines whether claims in the generated text are supported by retrieved evidence, formulated as a natural language inference (NLI) task.This study extends the label set for verdict prediction to capture claim-evidence relationships that humans would commonly interpret as supported or refuted, even in the absence of strict logical entailment or contradiction.It also constructs a Japanese dataset comprising 28,147 instances from two sources based on this extended label set.We analyze the causes of annotation disagreement and find that ambiguity in the boundary of acceptable inference, interpretive characteristics of negative cases, and incomplete information in the evidence affect annotation variability.Using this dataset, we evaluate the performance of prompt-based verdict prediction methods and show that prompts that explicitly elicit chain-of-thought reasoning improve F1 by 4 percentage points compared to baseline.