Triple

T2808568
Position Surface form Disambiguated ID Type / Status
Subject John Tillotson E54111 entity
Predicate familyName P18 FINISHED
Object Tillotson
Tillotson is an English surname most notably associated with John Tillotson, a 17th-century Archbishop of Canterbury.
E299048 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: Tillotson | Statement: [John Tillotson, familyName, Tillotson]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tillotson
Context triple: [John Tillotson, familyName, Tillotson]
  • A. Holley
    Holley is a surname most notably associated with Alexander Lyman Holley, a prominent 19th-century American engineer and steel industry pioneer.
  • B. Surtees
    Surtees is an English surname historically associated with notable figures in British literature, motorsport, and regional history.
  • C. The Diesel
    The Diesel is the nickname of Pro Football Hall of Fame running back John Riggins, renowned for his powerful, hard-charging rushing style with the Washington Redskins.
  • D. Roush-Yates Engines
    Roush-Yates Engines is a high-performance racing engine manufacturer and builder known for supplying competitive powerplants to top-level NASCAR and other motorsports teams.
  • E. GKN Sankey
    GKN Sankey was a British engineering and manufacturing company best known for producing military vehicles and automotive components.
  • 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: Tillotson
Triple: [John Tillotson, familyName, Tillotson]
Generated description
Tillotson is an English surname most notably associated with John Tillotson, a 17th-century Archbishop of Canterbury.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Tillotson
Target entity description: Tillotson is an English surname most notably associated with John Tillotson, a 17th-century Archbishop of Canterbury.
  • A. Holley
    Holley is a surname most notably associated with Alexander Lyman Holley, a prominent 19th-century American engineer and steel industry pioneer.
  • B. Surtees
    Surtees is an English surname historically associated with notable figures in British literature, motorsport, and regional history.
  • C. The Diesel
    The Diesel is the nickname of Pro Football Hall of Fame running back John Riggins, renowned for his powerful, hard-charging rushing style with the Washington Redskins.
  • D. Roush-Yates Engines
    Roush-Yates Engines is a high-performance racing engine manufacturer and builder known for supplying competitive powerplants to top-level NASCAR and other motorsports teams.
  • E. GKN Sankey
    GKN Sankey was a British engineering and manufacturing company best known for producing military vehicles and automotive components.
  • 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_69ab49dcee188190b5c6eca9ae9e3469 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abde2fdcf88190a52e515c166ea8f7 completed March 7, 2026, 8:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69afc678d6488190be73244b7e78f30c completed March 10, 2026, 7:21 a.m.
NEDg Description generation batch_69afc79deb2081908b0bfa5f14395503 completed March 10, 2026, 7:26 a.m.
NED2 Entity disambiguation (via description) batch_69afc82ac1148190a254415e336ba1e3 completed March 10, 2026, 7:28 a.m.
Created at: March 6, 2026, 9:59 p.m.