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Research · Intermediate

# Extract structured records from unstructured text

Parse messy documents and notes into schema-compliant records for your database.

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

- When you askReceive raw document text
- ThenMap entities to schema
- ResultConfirm before saving record

Turn unstructured notes, research papers, and document excerpts into clean, schema-aligned data objects. The bot extracts target attributes, verifies format adherence, and stages the structured records for review before inserting them into your knowledge base or database.

## Prompt to start with

BotBentoRecommended

```
When raw text or document excerpts are provided, analyze the material and extract entity names, descriptions, metadata, and relations matching the target schema. Validate that all required properties are populated and highlight any ambiguous values. Format the final output as a clean data record. Always ask for user confirmation before inserting, updating, or deleting any records in the database.
```

Copy

## Tools it uses

NotesDatabaseChat

## Works with

BotBento

## Tags

- parsing
- database
- schema
- research

## Source

[llama-cpp-agent: The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). Allowing users to ](https://github.com/Maximilian-Winter/llama-cpp-agent)
GitHub · checked 2026-10-16

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

By BotBento Editorial · Added 2026-10-16 · Updated 2026-10-16 · [Suggest a correction](/contact/)

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