Triple

T1472294
Position Surface form Disambiguated ID Type / Status
Subject Irving Babbitt E27161 entity
Predicate familyName P18 FINISHED
Object Babbitt
Babbitt is a surname most notably associated with American literary critic and academic Irving Babbitt.
 E168724 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: Babbitt | Statement: [Irving Babbitt, familyName, Babbitt]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Babbitt
Context triple: [Irving Babbitt, familyName, Babbitt]
  • A. The Whitworth
    The Whitworth is a prominent art gallery and museum in Manchester, England, renowned for its collections of fine art, textiles, and wallpapers and its integration with the surrounding park.
  • B. Woodward
    Woodward is a surname most prominently associated with American investigative journalist Bob Woodward, known for his reporting on the Watergate scandal.
  • C. Oberholtzer
    Oberholtzer is a German-origin surname, often associated with Mennonite and Amish families, that serves as a variant of the Overholt family name.
  • D. Bessemer
    Bessemer is a surname most notably associated with Sir Henry Bessemer, the English inventor who revolutionized steel production in the 19th century.
  • E. Bessemer
    Bessemer is an industrial city in Jefferson County, Alabama, historically known for its steelmaking and manufacturing.
  • 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: Babbitt
Triple: [Irving Babbitt, familyName, Babbitt]
Generated description
Babbitt is a surname most notably associated with American literary critic and academic Irving Babbitt.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Babbitt
Target entity description: Babbitt is a surname most notably associated with American literary critic and academic Irving Babbitt.
  • A. The Whitworth
    The Whitworth is a prominent art gallery and museum in Manchester, England, renowned for its collections of fine art, textiles, and wallpapers and its integration with the surrounding park.
  • B. Woodward
    Woodward is a surname most prominently associated with American investigative journalist Bob Woodward, known for his reporting on the Watergate scandal.
  • C. Oberholtzer
    Oberholtzer is a German-origin surname, often associated with Mennonite and Amish families, that serves as a variant of the Overholt family name.
  • D. Bessemer
    Bessemer is a surname most notably associated with Sir Henry Bessemer, the English inventor who revolutionized steel production in the 19th century.
  • E. Bessemer
    Bessemer is an industrial city in Jefferson County, Alabama, historically known for its steelmaking and manufacturing.
  • 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_69a496d25d6881909dbd84f86d763992 completed March 1, 2026, 7:43 p.m.
NER Named-entity recognition batch_69a4c5dc90e481908a4935f266bc7850 completed March 1, 2026, 11:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad15a74fbc8190b53511713032dc63 completed March 8, 2026, 6:22 a.m.
NEDg Description generation batch_69ad162d7d08819085108fa5bb33f40f completed March 8, 2026, 6:24 a.m.
NED2 Entity disambiguation (via description) batch_69ad16a5c76c8190a0bb3ccf5557b1b0 completed March 8, 2026, 6:26 a.m.
Created at: March 1, 2026, 8:01 p.m.