---
license: mit
language:
- en
pretty_name: "PII-TRACE: Multi-Turn PII NER Dataset"
task_categories:
- token-classification
size_categories:
- n<1K
tags:
- pii
- named-entity-recognition
- span-detection
- conversational
- multi-turn
configs:
- config_name: conversations
  default: true
  data_files:
  - split: train
    path: data/train.parquet
---

# PII-TRACE

PII-TRACE is a synthetic dataset for privacy-focused named entity recognition (NER) of personally identifiable information (PII), released as a 500-conversation subset of multi-turn dialogues with exact span annotations.

## Dataset summary

This release contains 500 conversations with 2,653 annotated PII spans across nine labels. Among them, 450 conversations contain PII spans and 50 contain none.

## Data format

Each record contains:

| Field | Type | Description |
|---|---|---|
| `id` | string | Unique record ID. |
| `turns` | list | Ordered turn objects containing `turn`, `user`, and `assistant`. |
| `spans` | list | Gold PII spans. An empty list indicates an empty control. |

Each item in `spans` contains:

| Field | Type | Description |
|---|---|---|
| `label` | string | PII label. |
| `turn` | integer | Zero-based index of the containing turn. |
| `source` | string | Message role containing the span (`user` or `assistant`). |
| `start` | integer | Inclusive Unicode code-point start offset in the selected message. |
| `end` | integer | Exclusive Unicode code-point end offset in the selected message. |
| `text` | string | Exact annotated substring. |

Do not trim or normalize message text before applying offsets.

## Labels

| Label | Description |
|---|---|
| `private_person` | Person name |
| `private_date` | Date |
| `private_url` | Link or route |
| `private_address` | Mailing or location address |
| `account_number` | Account or reference number |
| `private_email` | Email address |
| `private_phone` | Telephone number |
| `other_pii` | Other identifier |
| `secret` | Access code or credential-like value |

## Evaluation

Supply `turns` to the model and keep `spans` hidden. Report micro and macro exact typed-span precision, recall, and F1 over `(turn, source, start, end, label)`. Untyped exact-span F1 over `(turn, source, start, end)` may also be reported.

This subset may be used for training, evaluation, and error analysis in PII span detection research.

## Limitations

This is a controlled benchmark rather than an estimate of real-world PII prevalence or deployment-level performance. Its English conversations and identifier formats are more regular than production traffic, and current gold spans occur in user messages. The `secret` label covers credential-like sensitive values but does not assert that every such value is statutory PII.

## License

MIT.

## Citation

If you use or reference this work, please cite:

```bibtex
@article{zhang2026piitrace,
  title = {{PII-TRACE}: A Benchmark for Context-Aware {PII} Detection in Multi-Turn {LLM} Conversations},
  author = {Zhang, Kaiyuan and Wang, Chuan and Zhong, Joey and Fryzel, Paul and Polley, Kyle and Ma, Jerry and Li, Ninghui},
  journal = {arXiv preprint arXiv:2609.22200},
  year = {2026},
  url = {https://arxiv.org/abs/2609.22200}
}
```