The dataset was designed as a reusable Uzbek legal-domain named entity recognition (NER) resource for low-resource information extraction research. ⁸ The release covers 12 entity categories: PER, ORG, LOC, DATE, MONEY, POSITION, DOCNO, LAW, COURT, BANK, TIN, and CADASTRE. The main goal of the dataset construction process was to create a provenance-aware and human-reviewable resource that can support dataset curation, transparent reuse, and training support under provenance-aware conditions in Uzbek legal NLP. ⁹

The release was organized as a multi-layer package rather than as a single flat table. In particular, the dataset distinguishes between: 1.

A core benchmark-oriented subset containing the most reviewable and source-grounded records, and

This layered design was adopted to preserve methodological transparency and to prevent benchmark-oriented records from being mixed with augmentation-oriented material without explicit provenance tracking.

The dataset was compiled from Uzbek legal and quasi-legal texts collected from publicly accessible and reusable sources. Source selection was guided by legal relevance, practical reusability, and the need to cover both standard entity classes (such as persons, organizations, and locations) and legal-administrative entity classes (such as document numbers, legal references, tax identifiers, and cadastral identifiers). ¹¹

Because the target schema includes specialized labels that are not uniformly represented in public texts, the collection process was label-aware. High-frequency entity classes such as PER, ORG, and LOC were gathered from broader institutional and formal texts, whereas lower-frequency and domain-specific classes such as BANK, COURT, TIN, and CADASTRE required more targeted retrieval. ¹² Source-level provenance was preserved wherever possible through metadata fields such as Source_Name, Source_URL, Data_Origin, and Source_Group.

After source collection, candidate records were assembled in a label-wise manner rather than through a single uniform pipeline. Intermediate label-specific tables were created first and then merged into a unified release structure. ¹³ This approach made it possible to monitor class coverage, identify low-resource labels early, and perform targeted refinement where required.

During preprocessing, the dataset underwent several harmonization steps: •

Alignment of label-wise tables into a common schema;

Each record in the final package was represented as a row containing a text context (Sentence), an associated entity mention (Extracted_Text), a label (Label), and supporting provenance, verification, and usage metadata. Whenever recoverable, character-level offsets (Start_Char and End_Char) were also included to facilitate later conversion into stricter span-based sequence-labeling formats. The principal record-level fields included in the released dataset are summarized in Table 1. These fields describe not only the text and entity content of each record, but also its provenance, review status, and intended use within the release structure.

The table summarizes the principal text, provenance, review, and release-management fields included in the dataset package.

The dataset uses a 12-class entity schema designed specifically for Uzbek legal and quasi-legal documents. The selected labels were intended to balance practical legal relevance with annotation interpretability. In addition to general-domain categories such as PER, ORG, LOC, and DATE, the schema includes legal-administrative categories such as DOCNO, LAW, COURT, BANK, TIN, and CADASTRE, which are important for legal document analysis, structured extraction, and retrieval-oriented NLP applications.

To reduce ambiguity, the release documentation distinguishes between labels that may be superficially similar but functionally different in legal texts, such as ORG vs BANK, ORG vs COURT, DOCNO vs TIN, and DOCNO vs CADASTRE. ¹⁴ The full set of entity categories included in the release is summarized in Table 2. This overview clarifies the scope of the annotation schema and highlights the legal-domain relevance of the selected labels.

The table lists the 12 named entity categories included in the release together with their general interpretation and typical legal-domain use. Abbreviations: PER, person; ORG, organization; LOC, location; DOCNO, document number; LAW, legal reference; COURT, judicial institution; BANK, banking institution; TIN, tax identification number; CADASTRE, cadastral identifier.

The release was prepared through a review-oriented refinement workflow rather than as a fully finalized gold-standard benchmark. After merging and harmonization, the records were screened using a staged quality-control procedure. Priority was given to: •

Rows with missing extracted entity text;

Review decisions were recorded explicitly using categories such as keep, edit, drop, and move_to_augmented_only. The release also preserves row-level review metadata through fields such as Is_Verified, Verification_Status, Gold_Status, Eligibility, and Quality_Flag. This design makes the current version more transparent and easier to refine collaboratively in future iterations. ¹⁵

The present release should therefore be interpreted as a human-reviewed, gold-ready resource, rather than as a fully adjudicated final gold benchmark.

To support labels with limited naturally available coverage in public Uzbek legal texts, a controlled synthetic augmentation strategy was applied selectively. Synthetic rows were introduced only for lower-frequency classes where source-grounded examples were insufficient for practical experimental support. The augmentation process followed a template-based generation strategy, designed to preserve legal-domain plausibility and label clarity. ¹⁶

Synthetic records were explicitly marked in the metadata and were kept separate from benchmark-oriented material. These rows are intended for training support only and should not be treated as equivalent to manually reviewable source-grounded examples in evaluation settings.

The final dataset was released in four synchronized formats: XLSX, CSV, JSON, and JSONL. These formats were chosen to support different user needs: •

XLSX for manual inspection and metadata review,

In addition to the data files, the package includes supporting documentation such as a README, data dictionary, split description, known limitations, citation file, changelog, and license information. The main components of the released package and their practical roles are summarized in Table 3. This table makes the multi-format structure of the release explicit and shows how the package supports both manual inspection and machine-readable reuse.

The table summarizes the principal package components, their file formats, and their intended practical role in reuse. Abbreviations: XLSX, Microsoft Excel workbook; CSV, comma-separated values; JSON, JavaScript Object Notation; JSONL, line-delimited JSON; TXT, plain text; MD, Markdown.

The dataset preparation, harmonization, and export workflow was carried out using standard spreadsheet and scripting tools. Structured preprocessing and export operations were performed using Python, version 3.10 with standard data-processing libraries such as pandas, version 1.5.3 and json, openpyxl, and re (regular expressions). The scripts and supporting files used for preprocessing, entity extraction, and formatting for the Zenodo release are included in the associated Zenodo repository: https://doi.org/10.5281/zenodo.19682709. ¹⁷

No task-specific model training was required for the generation of the released tabular package itself; however, semi-automatic processing steps such as field normalization, recoverable span localization, and export conversion were performed using the above software environment. Parameters that may affect reproducibility include text normalization rules, row filtering criteria, provenance-based subset separation, and the logic used to assign Recommended_Split, Gold_Status, and Verification_Status fields. ¹⁸ The overall dataset construction and release workflow is illustrated in Figure 1.