Triple

T19708538
Position Surface form Disambiguated ID Type / Status
Subject Lord Millett E473277 entity
Predicate familyName P18 FINISHED
Object Millett
Millett is an English surname most notably associated with Lord Millett, a distinguished British judge and law lord.
 E1391595 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: Millett | Statement: [Lord Millett, familyName, Millett]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Millett
Context triple: [Lord Millett, familyName, Millett]
  • A. Trulaske
    Trulaske is the commonly used name for the Robert J. Trulaske, Sr. College of Business at the University of Missouri, a business school offering undergraduate and graduate programs in fields such as accounting, finance, and management.
  • B. Matta
    Matta is a surname most prominently associated with Thad Matta, a successful American college basketball coach known for his tenures at Xavier and Ohio State.
  • C. Matta
    Matta is a town located in Pakistan’s Swat District, known for its agricultural surroundings and scenic mountainous landscape.
  • D. Molinaro
    Molinaro is an Italian occupational surname, historically associated with millers and derived from the same root as "Molinero."
  • E. Cunlhat
    Cunlhat is a small rural commune in central France’s Puy-de-Dôme department, known for its traditional Auvergne countryside setting.
  • 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: Millett
Triple: [Lord Millett, familyName, Millett]
Generated description
Millett is an English surname most notably associated with Lord Millett, a distinguished British judge and law lord.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Millett
Target entity description: Millett is an English surname most notably associated with Lord Millett, a distinguished British judge and law lord.
  • A. Trulaske
    Trulaske is the commonly used name for the Robert J. Trulaske, Sr. College of Business at the University of Missouri, a business school offering undergraduate and graduate programs in fields such as accounting, finance, and management.
  • B. Matta
    Matta is a surname most prominently associated with Thad Matta, a successful American college basketball coach known for his tenures at Xavier and Ohio State.
  • C. Matta
    Matta is a town located in Pakistan’s Swat District, known for its agricultural surroundings and scenic mountainous landscape.
  • D. Molinaro
    Molinaro is an Italian occupational surname, historically associated with millers and derived from the same root as "Molinero."
  • E. Cunlhat
    Cunlhat is a small rural commune in central France’s Puy-de-Dôme department, known for its traditional Auvergne countryside setting.
  • 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_69d8e516dd048190a0b6c93ea3e71f58 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e642bc754481908bdf5bfad069aec8 completed April 20, 2026, 3:14 p.m.
NED1 Entity disambiguation (via context triple) batch_6a07ab9cbf0c8190a34ceeeb9960677b completed May 15, 2026, 11:26 p.m.
NEDg Description generation batch_6a07ac51fddc8190840698fb5cee0ea0 completed May 15, 2026, 11:29 p.m.
NED2 Entity disambiguation (via description) batch_6a07ae6493608190b8bc885ab8a916b2 completed May 15, 2026, 11:38 p.m.
Created at: April 10, 2026, 1:46 p.m.