Legal natural language processing (NLP) tasks often lack a single objective ground truth, as reflected in label variation across experts’ annotations. We test whether two uncertainty signals based on outputs of large language models (LLMs), sampling entropy and verbalized probability, can identify posts where legal experts disagree. On 1,564 German social media posts annotated by three legal experts for punishability under the German Criminal Code, we evaluate four open-weight LLMs, with reasoning either enabled or disabled, and observe a drop in F1 score on contested posts. Sampling distributions collapse to near-deterministic judgments in most conditions, while verbalized probabilities track expert disagreement in aggregate and support deferral of model errors. Neither signal, however, reliably identifies individual contested posts: both detect the model’s own errors (AUROC up to 0.83) better than expert disagreement (up to 0.76), and which signal is more informative depends strongly on model and activation of reasoning mode.

Government-issued travel advisories are used by citizens to get information about destination countries for tourism and other purposes such as temporary work stays or permanent relocation plans. However, qualitative evidence suggests that travel advisories may be influenced by considerations beyond current security situations. Systematic and rigorous quantitative analyses of advisories are scarce because relevant corpus data are not readily available and official government websites often provide practical obstacles. We validate a pipeline to generate a time-series cross-sectional dataset of government-issued travel advisories for three English-speaking issuing countries based on the Internet Archive’s Wayback Machine. Using official government data sources that are prohibited to be scraped and used for research, we illustrate that our approach provides (near-)complete coverage. The resulting corpus and code are intended to support downstream research on comparative risk communication, international relations, and text analysis using natural language processing methods.