---
dataset_info:
  features:
  - name: input
    dtype: string
  - name: output
    list:
    - name: dataset_mention
      struct:
      - name: dataset_name
        dtype: string
      - name: dataset_tag
        struct:
        - name: value
          dtype: string
        - name: confidence
          dtype: string
      - name: data_type
        struct:
        - name: value
          dtype: string
        - name: confidence
          dtype: string
      - name: acronym
        dtype: string
      - name: author
        dtype: string
      - name: producer
        dtype: string
      - name: publication_year
        dtype: string
      - name: reference_year
        dtype: string
      - name: reference_population
        dtype: string
      - name: geography
        dtype: string
      - name: description
        dtype: string
      - name: is_used
        struct:
        - name: value
          dtype: bool
        - name: confidence
          dtype: string
      - name: usage_context
        struct:
        - name: value
          dtype: string
        - name: confidence
          dtype: string
  splits:
  - name: train
    num_bytes: 474751
    num_examples: 403
  - name: test
    num_bytes: 49767
    num_examples: 46
  - name: eval
    num_bytes: 54930
    num_examples: 51
  download_size: 154032
  dataset_size: 579448
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
  - split: eval
    path: data/eval-*
license: mit
task_categories:
- token-classification
language:
- en
tags:
- GLiNER
- data-mentions
- World Bank
- PRWP
---

# World Bank PRWP - Refugee Data Manual Annotation

## Dataset Description

This dataset consists of manually annotated excerpts from text describing data sources. It is intended for training and evaluating Named Entity Recognition (NER) models (specifically designed for the 13-field GLiNER2 data-mention schema) to extract mentions of datasets, databases, surveys, censuses, and other data sources.

The dataset focuses on the PRWP (Poverty and Equity Global Practice) refugee data contexts from the World Bank.

### Organization

The dataset is divided into three splits:
- **train**: 403 multi-mention records used for training models.
- **eval**: 51 multi-mention records used for validation.
- **test**: 46 multi-mention records used to benchmark model performance.

### Data Instances

Each record corresponds to a text snippet (`input`) and contains a list of data mentions (`output`) complying with a strict 13-field JSON schema.
The schema enforces verbatim grounding, where each `dataset_name`, along with non-classification metadata fields (acronym, author, producer, description, etc.), must be an exact substring of the `input` text.

Features:
- `input`: The original text snippet.
- `output`: A list of objects containing:
  - `dataset_name`: The verbatim mention of the data source.
  - `dataset_tag`: Classification (named, descriptive, vague).
  - `data_type`: Inferred type of data (survey, census, administrative, database, indicator, geospatial, microdata, report, other).
  - `acronym`, `author`, `producer`, `description`, `geography`, `publication_year`, `reference_year`, `reference_population`: Contextual entity string metadata fields representing properties of the dataset.
  - `is_used`: Indication if this data source was utilized in the research/analysis.
  - `usage_context`: Role of the data source (primary, supporting, etc.).

### Quality Assurance

This ground-truth dataset underwent deep manual auditing and programmatic refinement:
- Corrected categorization and unified data tags.
- Verified 100% "verbatim" text grounding for spans.
- Dropped false-positive non-data mentions, such as bare years, general organization references, or methodology fragments.
- Detected and merged any duplicate entries or highly overlapping record snippets using Jaccard text similarity.

## Usage

This dataset is structurally aligned to be used directly to fine-tune GLiNER2 adapter modules or to evaluate general data-mention entity extraction pipelines.