Triple

T4123603
Position Surface form Disambiguated ID Type / Status
Subject Merthyr Tydfil College E92669 entity
Predicate regulatedBy P86 FINISHED
Object Estyn
Estyn is the education and training inspectorate for Wales, responsible for evaluating the quality and standards of schools, colleges, and other learning providers.
 E416351 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Estyn | Statement: [Merthyr Tydfil College, regulatedBy, Estyn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Estyn
Context triple: [Merthyr Tydfil College, regulatedBy, Estyn]
  • A. Tivissa
    Tivissa is a historic village in Catalonia, Spain, known for its scenic setting among the mountains of the Ribera d’Ebre region and its well-preserved medieval core.
  • B. Nesta
    Nesta is the middle name of legendary Jamaican reggae musician and cultural icon Bob Marley.
  • C. Gweru
    Gweru is a central Zimbabwean city that serves as the capital of the Midlands Province and an important commercial and transportation hub.
  • D. Goytre
    Goytre is a village and community located within the county borough of Neath Port Talbot in South Wales.
  • E. Geithain
    Geithain is a small town in the German state of Saxony, known for its historic center and regional rail connections.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Estyn
Triple: [Merthyr Tydfil College, regulatedBy, Estyn]
Generated description
Estyn is the education and training inspectorate for Wales, responsible for evaluating the quality and standards of schools, colleges, and other learning providers.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Estyn
Target entity description: Estyn is the education and training inspectorate for Wales, responsible for evaluating the quality and standards of schools, colleges, and other learning providers.
  • A. Tivissa
    Tivissa is a historic village in Catalonia, Spain, known for its scenic setting among the mountains of the Ribera d’Ebre region and its well-preserved medieval core.
  • B. Nesta
    Nesta is the middle name of legendary Jamaican reggae musician and cultural icon Bob Marley.
  • C. Gweru
    Gweru is a central Zimbabwean city that serves as the capital of the Midlands Province and an important commercial and transportation hub.
  • D. Goytre
    Goytre is a village and community located within the county borough of Neath Port Talbot in South Wales.
  • E. Geithain
    Geithain is a small town in the German state of Saxony, known for its historic center and regional rail connections.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69aed9685f70819086932777aec8d959 completed March 9, 2026, 2:30 p.m.
NER Named-entity recognition batch_69af0208903c8190a7f451a455d3e253 completed March 9, 2026, 5:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69b576b405548190affdd8bb108995b1 completed March 14, 2026, 2:54 p.m.
NEDg Description generation batch_69b577653214819093396a8601e52245 completed March 14, 2026, 2:57 p.m.
NED2 Entity disambiguation (via description) batch_69b57b90cb308190ba102f999795829c completed March 14, 2026, 3:15 p.m.
Created at: March 9, 2026, 3:41 p.m.