Triple

T15486883
Position Surface form Disambiguated ID Type / Status
Subject Project Blue Book E377069 entity
Predicate leadActor P1507 FINISHED
Object Michael Harney
Michael Harney is an American character actor best known for his roles in television series such as "Orange Is the New Black" and numerous crime and drama shows.
 E1167658 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: Michael Harney | Statement: [Project Blue Book, leadActor, Michael Harney]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Michael Harney
Context triple: [Project Blue Book, leadActor, Michael Harney]
  • A. Christopher Harter
    Christopher Harter is known primarily as the husband of British actor Jeremy Kemp.
  • B. Ben Harney
    Ben Harney is an American actor best known for his Tony Award–winning performance in the original Broadway production of the musical "Dreamgirls."
  • C. John Harron
    John Harron was an American film actor of the silent and early sound era, known for appearing in numerous supporting roles during the 1920s and 1930s.
  • D. Michael Harnett
    Michael Harnett is the birth name of Michael Hartnett, a prominent Irish poet known for his lyrical work in both English and Irish.
  • E. Michael Havers
    Michael Havers was a prominent British barrister and Conservative politician who served as Attorney General and later briefly as Lord Chancellor in the late 20th century.
  • 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: Michael Harney
Triple: [Project Blue Book, leadActor, Michael Harney]
Generated description
Michael Harney is an American character actor best known for his roles in television series such as "Orange Is the New Black" and numerous crime and drama shows.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Michael Harney
Target entity description: Michael Harney is an American character actor best known for his roles in television series such as "Orange Is the New Black" and numerous crime and drama shows.
  • A. Christopher Harter
    Christopher Harter is known primarily as the husband of British actor Jeremy Kemp.
  • B. Ben Harney
    Ben Harney is an American actor best known for his Tony Award–winning performance in the original Broadway production of the musical "Dreamgirls."
  • C. John Harron
    John Harron was an American film actor of the silent and early sound era, known for appearing in numerous supporting roles during the 1920s and 1930s.
  • D. Michael Harnett
    Michael Harnett is the birth name of Michael Hartnett, a prominent Irish poet known for his lyrical work in both English and Irish.
  • E. Michael Havers
    Michael Havers was a prominent British barrister and Conservative politician who served as Attorney General and later briefly as Lord Chancellor in the late 20th century.
  • 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_69d85cd21dcc81908646251b1c26ea00 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e03f8f71a08190a440ff19dcc65312 completed April 16, 2026, 1:46 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff5f29cee481908f0f81c4cc581f7f completed May 9, 2026, 4:22 p.m.
NEDg Description generation batch_69ff60188df48190a1cc891757a795d0 completed May 9, 2026, 4:26 p.m.
NED2 Entity disambiguation (via description) batch_69ff60938ef081908cd88cf8242bc785 completed May 9, 2026, 4:28 p.m.
Created at: April 10, 2026, 3:47 a.m.