Triple

T508895
Position Surface form Disambiguated ID Type / Status
Subject Telford Taylor E10561 entity
Predicate givenName P17 FINISHED
Object Telford
Telford is a given name most notably associated with Telford Taylor, the American lawyer and chief prosecutor at the Nuremberg Trials.
 E72346 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: Telford | Statement: [Telford Taylor, givenName, Telford]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Telford
Context triple: [Telford Taylor, givenName, Telford]
  • A. Wolverhampton
    Wolverhampton is a large industrial city in England’s West Midlands, known historically for its role in the coal, steel, and manufacturing industries.
  • B. Coventry
    Coventry is a historic city in England, best known for its medieval cathedral destroyed in World War II and its symbolic postwar reconciliation efforts.
  • C. Shrewsbury
    Shrewsbury is a historic market town in Shropshire, England, known for its well-preserved medieval streets and timber-framed buildings.
  • D. Crewe
    Crewe is a major railway town in Cheshire, England, historically known as a key junction on the British rail network and a center of railway engineering.
  • E. Milton Keynes
    Milton Keynes is a large, planned new town in Buckinghamshire, England, known for its grid road system, modern architecture, and extensive green spaces.
  • 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: Telford
Triple: [Telford Taylor, givenName, Telford]
Generated description
Telford is a given name most notably associated with Telford Taylor, the American lawyer and chief prosecutor at the Nuremberg Trials.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Telford
Target entity description: Telford is a given name most notably associated with Telford Taylor, the American lawyer and chief prosecutor at the Nuremberg Trials.
  • A. Wolverhampton
    Wolverhampton is a large industrial city in England’s West Midlands, known historically for its role in the coal, steel, and manufacturing industries.
  • B. Coventry
    Coventry is a historic city in England, best known for its medieval cathedral destroyed in World War II and its symbolic postwar reconciliation efforts.
  • C. Shrewsbury
    Shrewsbury is a historic market town in Shropshire, England, known for its well-preserved medieval streets and timber-framed buildings.
  • D. Crewe
    Crewe is a major railway town in Cheshire, England, historically known as a key junction on the British rail network and a center of railway engineering.
  • E. Milton Keynes
    Milton Keynes is a large, planned new town in Buckinghamshire, England, known for its grid road system, modern architecture, and extensive green spaces.
  • 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_69a2e848adf881908e5e04f7af030093 completed Feb. 28, 2026, 1:06 p.m.
NER Named-entity recognition batch_69a2f162f3888190b057ad04e40a30e2 completed Feb. 28, 2026, 1:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69a501bb88f88190b1de92ca77606d2f completed March 2, 2026, 3:19 a.m.
NEDg Description generation batch_69a503b501388190baed19e781c24b4d completed March 2, 2026, 3:27 a.m.
NED2 Entity disambiguation (via description) batch_69a5077cf14081909478ea1e0fd3eff5 completed March 2, 2026, 3:43 a.m.
Created at: Feb. 28, 2026, 1:12 p.m.