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metadata 
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

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_ids 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 2024 – 2026 58 58 1
Australia HF isaacus/high-court-of-australia-cases 2015 – 2025 30 30 1
Belgium juportal.be 2015 – 2025 55 55 1
Brazil scon.stj.jus.br 2002 – 2026 130 179 3
France Judilibre (PISTE) API 2015 – 2025 30 30 1
Georgia supremecourt.ge 2026 only 112 161 3
Germany HF openlegaldata/court-decisions-germany 2015 – 2022 130 180 3
Hong Kong legalref.judiciary.hk 2015 – 2026 10 10 1
India AWS Open Data Indian Supreme Court 2017 – 2023 24 24 1
Nepal nkp.gov.np 2021 – 2025 130 130 1
New Zealand justice.govt.nz JDO API 2015 – 2025 29 29 1
Philippines elibrary.judiciary.gov.ph 2024 – 2025 10 10 1
Serbia vrh.sud.rs 2023 – 2025 26 26 1
Singapore sgcaselaw.com 2025 – 2026 124 172 3
Spain poderjudicial.es 2025 – 2026 130 130 1
Switzerland HF voilaj/swiss-caselaw 2024 – 2025 130 190 3
Taiwan judgment.judicial.gov.tw 2026 only 130 180 3
United Kingdom caselaw.nationalarchives.gov.uk 2015 – 2025 130 190 3
United States HF 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 

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.