Triple

T4884856
Position Surface form Disambiguated ID Type / Status
Subject Hatfields & McCoys E109413 entity
Predicate starring P1507 FINISHED
Object Matt Barr
Matt Barr is an American actor known for his roles in television series such as "Hatfields & McCoys," "One Tree Hill," and "Valor."
 E478299 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: Matt Barr | Statement: [Hatfields & McCoys, starring, Matt Barr]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Matt Barr
Context triple: [Hatfields & McCoys, starring, Matt Barr]
  • A. Eric Bauza
    Eric Bauza is a Canadian voice actor and comedian best known for portraying iconic animated characters in modern Looney Tunes productions.
  • B. Jason Ritter
    Jason Ritter is an American actor known for his work in film and television, including roles in series like "Parenthood" and "Joan of Arcadia."
  • C. Seth Gabel
    Seth Gabel is an American actor known for his roles in television series such as "Fringe," "Salem," and "Nip/Tuck."
  • D. Joel Murray
    Joel Murray is an American actor and comedian known for his character roles in film and television, as well as for his voice work in animated projects.
  • E. Bill Durnan
    Bill Durnan was a Hall of Fame Canadian goaltender for the Montreal Canadiens in the 1940s, renowned for his ambidextrous catching ability and dominance in the early NHL.
  • 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: Matt Barr
Triple: [Hatfields & McCoys, starring, Matt Barr]
Generated description
Matt Barr is an American actor known for his roles in television series such as "Hatfields & McCoys," "One Tree Hill," and "Valor."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Matt Barr
Target entity description: Matt Barr is an American actor known for his roles in television series such as "Hatfields & McCoys," "One Tree Hill," and "Valor."
  • A. Eric Bauza
    Eric Bauza is a Canadian voice actor and comedian best known for portraying iconic animated characters in modern Looney Tunes productions.
  • B. Jason Ritter
    Jason Ritter is an American actor known for his work in film and television, including roles in series like "Parenthood" and "Joan of Arcadia."
  • C. Seth Gabel
    Seth Gabel is an American actor known for his roles in television series such as "Fringe," "Salem," and "Nip/Tuck."
  • D. Joel Murray
    Joel Murray is an American actor and comedian known for his character roles in film and television, as well as for his voice work in animated projects.
  • E. Bill Durnan
    Bill Durnan was a Hall of Fame Canadian goaltender for the Montreal Canadiens in the 1940s, renowned for his ambidextrous catching ability and dominance in the early NHL.
  • 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_69bd440f71348190b99938e59fb7f9a1 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6de3718881908521968fa6e6b444 completed March 20, 2026, 3:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69be6fbba1688190a812cac53992dece completed March 21, 2026, 10:15 a.m.
NEDg Description generation batch_69be707405008190ba1456544e8da593 completed March 21, 2026, 10:18 a.m.
NED2 Entity disambiguation (via description) batch_69be70e5537c8190b4db230932818a9c completed March 21, 2026, 10:20 a.m.
Created at: March 20, 2026, 1:27 p.m.