Triple

T8572549
Position Surface form Disambiguated ID Type / Status
Subject Tamara Tunie E202961 entity
Predicate previousSpouse P493 FINISHED
Object Greg Bouquett
Greg Bouquett is known as the former husband of American actress and director Tamara Tunie.
E742500 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: Greg Bouquett | Statement: [Tamara Tunie, previousSpouse, Greg Bouquett]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Greg Bouquett
Context triple: [Tamara Tunie, previousSpouse, Greg Bouquett]
  • A. Eric Gautier
    Eric Gautier is a French cinematographer renowned for his visually distinctive work on acclaimed international films and collaborations with prominent auteurs.
  • B. Kevin Grevioux
    Kevin Grevioux is an American actor, screenwriter, and comic book writer best known for co-creating the dark fantasy film franchise "Underworld."
  • C. Jeff Gourson
    Jeff Gourson is a film editor known for his work on movies such as the comedy "White Chicks."
  • D. Roger Taillibert
    Roger Taillibert was a French architect renowned for his innovative, sculptural modernist designs, most famously the Olympic Stadium in Montreal.
  • E. Charles LeMaire
    Charles LeMaire was an American costume designer renowned for his work in Hollywood’s Golden Age, earning multiple Academy Awards for his contributions to classic films.
  • 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: Greg Bouquett
Triple: [Tamara Tunie, previousSpouse, Greg Bouquett]
Generated description
Greg Bouquett is known as the former husband of American actress and director Tamara Tunie.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Greg Bouquett
Target entity description: Greg Bouquett is known as the former husband of American actress and director Tamara Tunie.
  • A. Eric Gautier
    Eric Gautier is a French cinematographer renowned for his visually distinctive work on acclaimed international films and collaborations with prominent auteurs.
  • B. Kevin Grevioux
    Kevin Grevioux is an American actor, screenwriter, and comic book writer best known for co-creating the dark fantasy film franchise "Underworld."
  • C. Jeff Gourson
    Jeff Gourson is a film editor known for his work on movies such as the comedy "White Chicks."
  • D. Roger Taillibert
    Roger Taillibert was a French architect renowned for his innovative, sculptural modernist designs, most famously the Olympic Stadium in Montreal.
  • E. Charles LeMaire
    Charles LeMaire was an American costume designer renowned for his work in Hollywood’s Golden Age, earning multiple Academy Awards for his contributions to classic films.
  • 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_69ca8327b0a881908606ff860713964d completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbea43843c8190ac2224d427bb7a75 completed March 31, 2026, 3:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce898cf8648190b52758b6ecf2959b completed April 2, 2026, 3:21 p.m.
NEDg Description generation batch_69ce8a9df47c81909ba9ef8dff1db7b1 completed April 2, 2026, 3:26 p.m.
NED2 Entity disambiguation (via description) batch_69ce8b48841c8190bcf11aeb25355649 completed April 2, 2026, 3:29 p.m.
Created at: March 30, 2026, 6:21 p.m.