# Business Entity Resolution Dataset - Embedding Friendly Scenario

Scenario: `data-splink`

This synthetic dataset is designed for Vietnamese text-based Entity Resolution / Record Linkage research.
It intentionally weakens exact identifiers and creates cross-source textual variation so that language embeddings can contribute beyond traditional fuzzy matching.

## Design choices

- High `tax_code` missing/noisy rate.
- Cross-source schema mismatch.
- Vietnamese text variants: missing diacritics, token merge, token order swap, legal type abbreviation, English translation, short-name-only variants.
- Hard negatives: similar names, same address, same representative, same short name across different true entities.

## Important files

- `canonical/canonical_business_entity.csv`: synthetic clean entity layer.
- `observed/*.csv`: raw source tables with different schemas.
- `evaluation/ground_truth_entity_map.csv`: record-to-entity ground truth.
- `evaluation/evaluation_labels.csv`: pair-level labels for model evaluation.
- `metadata/dataset_summary.json`: quality summary.

This notebook exports raw source tables only. It does not create a linkage view in advance.
The linkage view should be created later inside the matching pipeline notebook.