Triple

T713259
Position Surface form Disambiguated ID Type / Status
Subject John Harbison E14255 entity
Predicate familyName P18 FINISHED
Object Harbison
Harbison is a surname most notably associated with American composer John Harbison, known for his contributions to contemporary classical music.
 E97277 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: Harbison | Statement: [John Harbison, familyName, Harbison]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Harbison
Context triple: [John Harbison, familyName, Harbison]
  • A. Hayes
    Hayes is a suburban district in southeast London, England, known for its residential character and green spaces within the London Borough of Bromley.
  • B. Brewster
    Brewster is the given name of Brewster Kahle, an American computer engineer and digital librarian best known as the founder of the Internet Archive.
  • C. Brewster
    Brewster is a coastal town on Cape Cod in Massachusetts known for its scenic beaches, historic charm, and bayside conservation lands.
  • D. Brewster
    Brewster is a small hamlet and census-designated place in Putnam County, New York, known for its historic downtown and role as a local commercial and transportation hub.
  • E. Milhous
    Milhous is the distinctive middle name of Richard Nixon, the 37th president of the United States.
  • 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: Harbison
Triple: [John Harbison, familyName, Harbison]
Generated description
Harbison is a surname most notably associated with American composer John Harbison, known for his contributions to contemporary classical music.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Harbison
Target entity description: Harbison is a surname most notably associated with American composer John Harbison, known for his contributions to contemporary classical music.
  • A. Hayes
    Hayes is a suburban district in southeast London, England, known for its residential character and green spaces within the London Borough of Bromley.
  • B. Brewster
    Brewster is the given name of Brewster Kahle, an American computer engineer and digital librarian best known as the founder of the Internet Archive.
  • C. Brewster
    Brewster is a coastal town on Cape Cod in Massachusetts known for its scenic beaches, historic charm, and bayside conservation lands.
  • D. Brewster
    Brewster is a small hamlet and census-designated place in Putnam County, New York, known for its historic downtown and role as a local commercial and transportation hub.
  • E. Milhous
    Milhous is the distinctive middle name of Richard Nixon, the 37th president of the United States.
  • 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_69a4934a36e081909e7abef98b898a4e completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a55ee4fc81909358659ec3bc435f completed March 1, 2026, 8:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69a76d71a25c81908de9b9e59affb79f completed March 3, 2026, 11:23 p.m.
NEDg Description generation batch_69a78586d5a48190a1423bb4a8fc86b9 completed March 4, 2026, 1:06 a.m.
NED2 Entity disambiguation (via description) batch_69a785db28d48190a8f945598a3396d6 completed March 4, 2026, 1:07 a.m.
Created at: March 1, 2026, 7:36 p.m.