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Datasets:
BSVGK
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Text_to_KG_Construction_Dataset

Tasks:
Text Generation
Token Classification
Languages:
English
Size:
10K<n<100K
Tags:
knowledge-graph
rdf
information-extraction
uk-government-contracts
procurement
text-to-kg
License:
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metadata 
language:
  - en
license: mit
tags:
  - knowledge-graph
  - rdf
  - information-extraction
  - uk-government-contracts
  - procurement
  - text-to-kg
  - nlp
  - trustworthy-ai
task_categories:
  - text-generation
  - token-classification
size_categories:
  - 10K<n<100K

Text-to-KG Construction Dataset (UK Government Contracts)

Dataset Summary

This dataset contains 9,244 verified UK government procurement contracts paired with structured RDF knowledge graph triples, constructed for the task of automated Text-to-KG extraction. It was developed as part of a UEL–Depixen industrial placement research project focused on building trustworthy, hallucination-free domain-specific SLMs.

This dataset was used to fine-tune:

Dataset Details

Property Value
Domain UK Government Procurement Contracts
Total Samples 9,244 training + 1,387 test
Format Contract text → RDF Triples
Language English
Source UK Government procurement data
License MIT

Dataset Structure

Each sample contains:

{ "input": "Raw UK government contract text...", "output": [ {"subject": "entity_1", "predicate": "relation", "object": "entity_2"}, {"subject": "entity_1", "predicate": "relation", "object": "entity_3"} ] }

Construction Process

  1. Data Collection — UK government procurement contracts collected from public sources
  2. Preprocessing — Cleaning, deduplication, and normalisation of contract text
  3. Triple Extraction — Manual and automated RDF triple annotation
  4. Verification — Each triple verified against source contract text
  5. Quality Control — Dual-level hallucination check (L1: relation validity, L2: entity grounding)

Hallucination Evaluation Framework

This dataset was evaluated using a novel dual-level hallucination framework:

  • L1 — Relation Validity: All relations verified against a predefined ontology
  • L2 — Entity Grounding: All entities grounded in the source contract text

This ensured zero hallucination in the fine-tuned Phi-3.5 model across 1,387 unseen test contracts.

Models Trained on This Dataset

Model F1 BERTScore Hallucination Rate
Phi-3.5 Mini Instruct (LoRA) 0.9954 0.9997 0.00%
Gemma 2 2B IT (QLoRA) competitive competitive higher

Intended Use

  • Training SLMs for knowledge graph construction
  • Research in trustworthy and hallucination-free NLP
  • Information extraction from legal and procurement documents
  • RDF triple generation for semantic web applications

Out of Scope

  • Non-English contracts
  • Contracts outside UK government procurement domain
  • General purpose NLP tasks

Citation

@misc{bubathula2026texttokg_dataset, author = {Sai Venkata Gopala Krishna Bubathula}, title = {Text-to-KG Construction Dataset: UK Government Procurement Contracts for RDF Triple Extraction}, year = {2026}, publisher = {HuggingFace}, url = {https://huggingface.co/datasets/BSVGK/Text_to_KG_Construction_Dataset}, institution = {University of East London & Depixen} }

Developer

Sai Venkata Gopala Krishna Bubathula

  • 🎓 MSc Big Data Technologies, University of East London
  • 🏢 AI Engineer — UEL–Depixen Industrial Placement
  • 🔗 GitHub
  • 🔗 LinkedIn
  • 🔗 HuggingFace