Triple

T3885529
Position Surface form Disambiguated ID Type / Status
Subject Bad Boy for Life E92930 entity
Predicate performer P1363 FINISHED
Object Mark Curry
Mark Curry is an American comedian and actor best known for starring in the 1990s sitcom "Hangin' with Mr. Cooper."
 E299952 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: Mark Curry | Statement: [Bad Boy for Life, performer, Mark Curry]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mark Curry
Context triple: [Bad Boy for Life, performer, Mark Curry]
  • A. Mark Curry
    Mark Curry is an American stand-up comedian and actor best known for starring in the 1990s sitcom "Hangin' with Mr. Cooper."
  • B. Ed Scott
    Ed Scott is a technology entrepreneur best known as a co-founder of BEA Systems, a major enterprise software company later acquired by Oracle.
  • C. Christian Broun
    Christian Broun was a Scottish noblewoman best known as the mother of James Broun-Ramsay, 1st Marquess of Dalhousie, who served as Governor-General of India in the mid-19th century.
  • D. Dan Hughes
    Dan Hughes is an American basketball coach best known for leading the WNBA’s Seattle Storm to a championship and for his long, successful career coaching multiple WNBA franchises.
  • E. Ron Cook
    Ron Cook is a British character actor known for his extensive work in film, television, and theatre, often portraying supporting yet memorable roles.
  • 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: Mark Curry
Triple: [Bad Boy for Life, performer, Mark Curry]
Generated description
Mark Curry is an American comedian and actor best known for starring in the 1990s sitcom "Hangin' with Mr. Cooper."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mark Curry
Target entity description: Mark Curry is an American comedian and actor best known for starring in the 1990s sitcom "Hangin' with Mr. Cooper."
  • A. Mark Curry chosen
    Mark Curry is an American stand-up comedian and actor best known for starring in the 1990s sitcom "Hangin' with Mr. Cooper."
  • B. Ed Scott
    Ed Scott is a technology entrepreneur best known as a co-founder of BEA Systems, a major enterprise software company later acquired by Oracle.
  • C. Christian Broun
    Christian Broun was a Scottish noblewoman best known as the mother of James Broun-Ramsay, 1st Marquess of Dalhousie, who served as Governor-General of India in the mid-19th century.
  • D. Dan Hughes
    Dan Hughes is an American basketball coach best known for leading the WNBA’s Seattle Storm to a championship and for his long, successful career coaching multiple WNBA franchises.
  • E. Ron Cook
    Ron Cook is a British character actor known for his extensive work in film, television, and theatre, often portraying supporting yet memorable roles.
  • F. None of above.

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_69aed9697de0819087c2559295ff3d12 completed March 9, 2026, 2:30 p.m.
NER Named-entity recognition batch_69aeec92cc548190b88b899299e5ccdc completed March 9, 2026, 3:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5125bee048190ba7553797e9fd254 completed March 14, 2026, 7:46 a.m.
NEDg Description generation batch_69b512e3721c8190accd26499191c153 completed March 14, 2026, 7:48 a.m.
NED2 Entity disambiguation (via description) batch_69b513618b888190acda94dcc91d24d2 completed March 14, 2026, 7:50 a.m.
Created at: March 9, 2026, 3:20 p.m.