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Research · Intermediate

# Auditing entity recognition and hallucinations across models

Query public models to evaluate how accurately your identity or brand is represented

[Use with BotBento ](https://app.botbento.com/?ref=usecases&utm_source=botbento.com&utm_medium=usecases&utm_campaign=auditing-entity-recognition-and-hallucinations-across-models)Copy promptShare[Markdown](/text/usecases/auditing-entity-recognition-and-hallucinations-across-models/index.md)

Opens BotBento. Paste the prompt into a bot; nothing is preconfigured.

- When you askProvide target entity and background
- ThenQuery models and analyze responses
- ResultDeliver entity audit report

Check how public language models represent an identity, brand, or persona. The bot queries various model endpoints with standardized biographical prompts, clusters and evaluates the claims against verified facts, and compiles a report flagging accurate knowledge versus common hallucinations.

## Prompt to start with

BotBentoRecommended

```
When given a subject name and brief biographical ground truth, submit structured recognition probes across configured model endpoints. Record each model's output, measure the presence of verifiable biographical details, and highlight fictitious claims or hallucinations. Summarize the findings into a clear audit document. Ask for user confirmation before exporting or sharing the report externally.
```

Copy

## Tools it uses

BrowserDocsChat

## Works with

BotBento

## Tags

- reputation
- hallucinations
- evaluation
- auditing

## Source

[Show HN: Are You in the Weights?](https://news.ycombinator.com/item?id=48591348)
Hacker News · checked 2026-10-15

Summarized in our own words. Any results described there are the authors’ own, not measured by BotBento.

By BotBento Editorial · Added 2026-10-15 · Updated 2026-10-15 · [Suggest a correction](/contact/)

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