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---
license: mit
language:
 - en
 - de
 - fr
 - it
 - es
 - pt
 - hy
 - ka
 - ne
 - sr
 - zh
 - fil
 - nl
pretty_name: "LEGEX Goldensets: Expert-Coded Review-Table Annotations"
size_categories:
 - 1K<n<10K
task_categories:
 - text-classification
 - token-classification
 - text-generation
tags:
 - legal
 - benchmark
 - civil-judgments
 - information-extraction
 - llm-evaluation
 - cross-jurisdictional
dataset_info:
 - config_name: default
 features:
 - name: case_id
 dtype: string
 - name: link
 dtype: string
 - name: full_text
 dtype: string
 - name: legal_subject_judgement
 dtype: string
 - name: trial_start_date
 dtype: date32
 - name: trial_end_date
 dtype: date32
 - name: dispute_value_nominal
 dtype: string
 - name: Currency_dispute_value_nominal
 dtype: string
 - name: plaintiff_loosing_share
 dtype: float64
 - name: court_cost_awarded_nominal
 dtype: float64
 - name: Currency_court_cost_awarded_nominal
 dtype: string
 - name: party_compensation_awarded_nominal
 dtype: float64
 - name: Currency_party_compensation_awarded_nominal
 dtype: string
 - name: plaintiffs_all_count
 dtype: int64
 - name: defendants_all_count
 dtype: int64
 - name: plaintiff_no1_ISIC1_industry_category
 dtype: string
 - name: defendant_no1_ISIC1_industry_category
 dtype: string
 - name: comment
 dtype: string
 - name: original_input
 dtype: string
 - name: annotator_id
 dtype: string
configs:
 - config_name: default
 data_files:
 - split: armenia
 path: data/am/goldenset_am.jsonl
 - split: australia
 path: data/au/goldenset_au.jsonl
 - split: belgium
 path: data/be/goldenset_be.jsonl
 - split: brazil
 path: data/br/goldenset_br.jsonl
 - split: france
 path: data/fr/goldenset_fr.jsonl
 - split: georgia
 path: data/ge/goldenset_ge.jsonl
 - split: germany
 path: data/de/goldenset_de.jsonl
 - split: hong_kong
 path: data/hk/goldenset_hk.jsonl
 - split: india
 path: data/in/goldenset_in.jsonl
 - split: nepal
 path: data/np/goldenset_np.jsonl
 - split: new_zealand
 path: data/nz/goldenset_nz.jsonl
 - split: philippines
 path: data/ph/goldenset_ph.jsonl
 - split: serbia
 path: data/rs/goldenset_rs.jsonl
 - split: singapore
 path: data/sg/goldenset_sg.jsonl
 - split: spain
 path: data/es/goldenset_es.jsonl
 - split: switzerland
 path: data/ch/goldenset_ch.jsonl
 - split: taiwan
 path: data/tw/goldenset_tw.jsonl
 - split: united_kingdom
 path: data/uk/goldenset_uk.jsonl
 - split: united_states
 path: data/us/goldenset_us.jsonl
---
# LEGEX Goldensets: Expert-Coded Review-Table Annotations
This repository contains the expert-coded gold annotations for the LEGEX
benchmark of civil-judgment review-table extraction. 1,548 judgments across
19 jurisdictions have been annotated by hand against a shared 14-field schema
covering monetary outcomes, cost allocation, party structure, and industry
classification. Including independent secondary re-annotations, the release
holds 1,974 annotation rows.
## Dataset summary
Legal review-table systems are increasingly used to extract structured facts
from judgments, but there is little public evidence on their reliability in
cross-jurisdictional legal settings. LEGEX is an expert-coded benchmark for
civil-judgment review-table extraction. The current release contains an
eight-jurisdiction core benchmark with at least 100 judgments per jurisdiction,
each coded by two or more independent experts on a 28–30 case overlap and
eleven preview jurisdictions for testing schema portability. The re-annotated
overlap enables inter-annotator agreement (IAA) analysis on the core set.
The word cloud below shows the topical spread of the free-text
`legal_subject_judgement` labels over all annotated judgments (underscores
stripped, stopwords and generic terms such as "law" removed):
![Word cloud of the normalized legal-subject labels](assets/legal_subject_wordcloud.png)
## Schema
Each line in `data/<cc>/goldenset_<cc>.jsonl` is a JSON object with these
keys:
| Key | Type | Description |
|-----|-----|---------------------------------------------------------------------------------------------------------------------------------|
| `case_id` | string | Identifier within the source database. |
| `link` | string | URL to the original judgment. |
| `full_text` | string \| null | Full judgment text used as model input. |
| `legal_subject_judgement` | string | Short English subject of the case. Acts as the "this row has been substantively reviewed" marker. |
| `trial_start_date` | YYYY-MM-DD \| null | Trial start date. |
| `trial_end_date` | YYYY-MM-DD \| null | Decision date. |
| `dispute_value_nominal` | string \| null | Amount in dispute as a string (e.g. `"150000"`) or the literal `"nonpecuniary"`. |
| `Currency_dispute_value_nominal` | string \| null | ISO-4217 currency code. |
| `plaintiff_loosing_share` | number [0, 1] \| null | Plaintiff's losing share. |
| `court_cost_awarded_nominal` | number \| null | Court fees awarded. |
| `Currency_court_cost_awarded_nominal` | string \| null | ISO-4217 currency code. |
| `party_compensation_awarded_nominal` | number \| null | Party compensation awarded. |
| `Currency_party_compensation_awarded_nominal` | string \| null | ISO-4217 currency code. |
| `plaintiffs_all_count` | integer \| null | Number of plaintiffs. |
| `defendants_all_count` | integer \| null | Number of defendants. |
| `plaintiff_no1_ISIC1_industry_category` | string \| null | ISIC Section A–U for the first plaintiff. |
| `defendant_no1_ISIC1_industry_category` | string \| null | ISIC Section A–U for the first defendant. |
A row is included when `legal_subject_judgement` is populated (the marker the
annotator used to flag a row as substantively reviewed). Cells left empty by
the annotator are stored as `null`. In the raw JSONL the trial dates are
`YYYY-MM-DD` strings. The declared dataset features type them as `date32`, so
the `datasets` library returns them as date objects.
### Traceability fields
Every record additionally carries provenance fields so each value is
auditable:
| Key | Type | Description |
|-----|-----|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `annotator_id` | string | Pseudonymous, salted-hash id of the coder, stable per person across jurisdictions. |
| `comment` | string \| null | Human-readable note listing every field changed during sanitization, e.g. `"The trial_end_date was sanitized from '2026_03_19' to '2026-03-19'."`, `null` when the record needed no change. |
| `original_input` | string | JSON object mapping each changed field to its 1:1 pre-sanitization value, `"{}"` when nothing changed. This lets any consumer reconstruct the raw value. |
### Primary and secondary annotations
Jurisdictions that received an independent re-annotation contain both the
primary annotator's rows and the secondary annotators' rows, distinguished by
`annotator_id`. Within each file all primary rows come first and re-annotation
rows are appended after them, so **the first row per `case_id` is the primary
gold annotation**. For benchmark evaluation, deduplicate to the primary rows.
For inter-annotator agreement, group the duplicated `case_id`s by
`annotator_id`.
## Jurisdictions
### Highest civil court per jurisdiction
| Jurisdiction | Highest court (civil jurisdiction) | Language(s) |
|-----|-----|-----|
| Armenia | Court of Cassation (Civil Chamber) | Armenian |
| Australia | High Court of Australia | English |
| Belgium | Cour de cassation / Hof van Cassatie | French, Dutch |
| Brazil | Superior Tribunal de Justiça | Portuguese |
| France | Cour de cassation | French |
| Georgia | Supreme Court of Georgia | Georgian |
| Germany | Bundesgerichtshof | German |
| Hong Kong | Court of Final Appeal | English, Chinese |
| India | Supreme Court of India | English |
| Nepal | Supreme Court of Nepal | Nepali |
| New Zealand | Court of Appeal / High Court | English |
| Philippines | Supreme Court of the Philippines | English, Filipino |
| Serbia | Supreme Court of Cassation | Serbian |
| Singapore | Supreme Court (incl. SICC) | English |
| Spain | Tribunal Supremo (Sala de lo Civil) | Spanish |
| Switzerland | Federal Supreme Court | German, French, Italian |
| Taiwan | Supreme Court | Chinese |
| United Kingdom | Supreme Court of the United Kingdom | English |
| United States | Supreme Court of the United States | English |
### Data sources, judgment counts and annotation rows
`# judgments` counts unique expert-coded cases, `# rows` additionally counts
independent secondary re-annotations of the same cases.
| Jurisdiction | Data source | Time span | # judgments | # rows | Annotators |
|-----|-----|-----|-----|-----|-----|
| Armenia | [cassationcourt.am](https://www.cassationcourt.am) | 2024 – 2026 | 58 | 58 | 1 |
| Australia | [HF isaacus/high-court-of-australia-cases](https://huggingface.co/datasets/isaacus/high-court-of-australia-cases) | 2015 – 2025 | 30 | 30 | 1 |
| Belgium | [juportal.be](https://juportal.be) | 2015 – 2025 | 55 | 55 | 1 |
| Brazil | [scon.stj.jus.br](https://scon.stj.jus.br) | 2002 – 2026 | 130 | 179 | 3 |
| France | [Judilibre (PISTE) API](https://api.piste.gouv.fr/cassation/judilibre) | 2015 – 2025 | 30 | 30 | 1 |
| Georgia | [supremecourt.ge](https://www.supremecourt.ge) | 2026 only | 112 | 161 | 3 |
| Germany | [HF openlegaldata/court-decisions-germany](https://huggingface.co/datasets/openlegaldata/court-decisions-germany) | 2015 – 2022 | 130 | 180 | 3 |
| Hong Kong | [legalref.judiciary.hk](https://legalref.judiciary.hk) | 2015 – 2026 | 10 | 10 | 1 |
| India | [AWS Open Data Indian Supreme Court](https://registry.opendata.aws/indian-supreme-court-judgments) | 2017 – 2023 | 24 | 24 | 1 |
| Nepal | [nkp.gov.np](https://nkp.gov.np) | 2021 – 2025 | 130 | 130 | 1 |
| New Zealand | [justice.govt.nz JDO API](https://www.justice.govt.nz/jdo-search-api) | 2015 – 2025 | 29 | 29 | 1 |
| Philippines | [elibrary.judiciary.gov.ph](https://elibrary.judiciary.gov.ph) | 2024 – 2025 | 10 | 10 | 1 |
| Serbia | [vrh.sud.rs](https://vrh.sud.rs) | 2023 – 2025 | 26 | 26 | 1 |
| Singapore | [sgcaselaw.com](https://sgcaselaw.com) | 2025 – 2026 | 124 | 172 | 3 |
| Spain | [poderjudicial.es](https://www.poderjudicial.es) | 2025 – 2026 | 130 | 130 | 1 |
| Switzerland | [HF voilaj/swiss-caselaw](https://huggingface.co/datasets/voilaj/swiss-caselaw) | 2024 – 2025 | 130 | 190 | 3 |
| Taiwan | [judgment.judicial.gov.tw](https://judgment.judicial.gov.tw) | 2026 only | 130 | 180 | 3 |
| United Kingdom | [caselaw.nationalarchives.gov.uk](https://caselaw.nationalarchives.gov.uk) | 2015 – 2025 | 130 | 190 | 3 |
| United States | [HF free-law/Caselaw_Access_Project](https://huggingface.co/datasets/free-law/Caselaw_Access_Project) | 2020 – 2026 | 130 | 190 | 3 |
Total: 1,548 expert-coded judgments, 1,974 annotation rows.
Core benchmark (≥ 100 expert-coded judgments, coded by ≥ 2 independent
annotators): Brazil, Georgia, Germany, Singapore, Switzerland, Taiwan,
United Kingdom, United States. In each core jurisdiction 28–30 cases were
independently re-annotated by one or two additional experts.
Preview jurisdictions: the remaining eleven (Armenia, Australia, Belgium,
France, Hong Kong, India, Nepal, New Zealand, Philippines, Serbia, Spain).
Nepal reaches 130 cases but is single-annotated and therefore remains
preview.
## Loading
```python
from datasets import load_dataset
# Single jurisdiction
ds = load_dataset("legexbenchmark/goldensets", split="switzerland")
ds = load_dataset("legexbenchmark/goldensets", split="united_states")
# All jurisdictions
from datasets import concatenate_datasets
splits = [
 "armenia", "australia", "belgium", "brazil", "france", "georgia",
 "germany", "hong_kong", "india", "nepal", "new_zealand", "philippines",
 "serbia", "singapore", "spain", "switzerland", "taiwan",
 "united_kingdom", "united_states",
]
all_rows = concatenate_datasets([
 load_dataset("legexbenchmark/goldensets", split=s) for s in splits
])
```
## Limitations
- Sample sizes: Nine of the nineteen jurisdictions have fewer than 100
 expert-coded judgments; Nepal and Spain reach 100+ cases but are
 single-annotated. These eleven are marked as preview and intended for
 schema-portability checks rather than per-jurisdiction performance claims.
- Time coverage: Source databases vary considerably (Georgia and Taiwan are
 2026-only because earlier years were not freely available).
- Schema portability: the 14 fields were designed against civil judgments
 in common-law and Western European civil-law systems. Some fields
 (e.g. `plaintiff_loosing_share`, ISIC categorisation) may not be a natural
 fit for every jurisdiction.
- Court selection: For Brazil, there are judgements that are not from the
 highest possible court. This can be inferred with the case id. The Spain
 goldenset draws mainly on the Tribunal Constitucional and the Tribunal
 Económico-Administrativo Central rather than the Tribunal Supremo (Sala de
 lo Civil); the court can likewise be inferred from the case_id.
- Full-text quality: Text was extracted from heterogeneous sources (HTML,
 PDF, API JSON). Layout artefacts and OCR errors are possible.
## Citation
Anonymous submission to the ICML 2026 AI for Law workshop. Citation block
will be added after the camera-ready release.
## License
MIT.