Triple

T3949364
Position Surface form Disambiguated ID Type / Status
Subject Ken Follett E84825 entity
Predicate familyName P18 FINISHED
Object Follett
Follett is a surname most prominently associated with British author Ken Follett, known for his bestselling historical and thriller novels.
 E401392 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: Follett | Statement: [Ken Follett, familyName, Follett]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Follett
Context triple: [Ken Follett, familyName, Follett]
  • A. Faulks
    Faulks is the surname of British novelist and journalist Sebastian Faulks, best known for his historical and literary fiction.
  • B. Rutledge
    Rutledge is a surname of English and Scottish origin borne by various notable individuals in politics, law, and other fields.
  • C. Rutledge
    Rutledge is a small town in eastern Tennessee that serves as the county seat of Grainger County within the Knoxville metropolitan area.
  • D. Darrow
    Darrow is a surname most famously associated with Clarence Darrow, the prominent American lawyer and civil libertarian known for high-profile cases in the early 20th century.
  • E. Magruder
    Magruder is a surname most notably associated with Jeb Stuart Magruder, a key figure in the Watergate scandal during the Nixon administration.
  • 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: Follett
Triple: [Ken Follett, familyName, Follett]
Generated description
Follett is a surname most prominently associated with British author Ken Follett, known for his bestselling historical and thriller novels.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Follett
Target entity description: Follett is a surname most prominently associated with British author Ken Follett, known for his bestselling historical and thriller novels.
  • A. Faulks
    Faulks is the surname of British novelist and journalist Sebastian Faulks, best known for his historical and literary fiction.
  • B. Rutledge
    Rutledge is a surname of English and Scottish origin borne by various notable individuals in politics, law, and other fields.
  • C. Rutledge
    Rutledge is a small town in eastern Tennessee that serves as the county seat of Grainger County within the Knoxville metropolitan area.
  • D. Darrow
    Darrow is a surname most famously associated with Clarence Darrow, the prominent American lawyer and civil libertarian known for high-profile cases in the early 20th century.
  • E. Magruder
    Magruder is a surname most notably associated with Jeb Stuart Magruder, a key figure in the Watergate scandal during the Nixon administration.
  • 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_69aed934fbfc8190847068e4546de963 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aef910ecc08190a4fca89bcf063e0c completed March 9, 2026, 4:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5339feb4c8190ad96a5680cb5294e completed March 14, 2026, 10:08 a.m.
NEDg Description generation batch_69b5346fb3648190b2b72e10179588ed completed March 14, 2026, 10:11 a.m.
NED2 Entity disambiguation (via description) batch_69b534e98b8081909c2b71ff20fe0bb4 completed March 14, 2026, 10:14 a.m.
Created at: March 9, 2026, 3:30 p.m.