Triple

T18905770
Position Surface form Disambiguated ID Type / Status
Subject Federal-Mogul E462457 entity
Predicate brand P1500 FINISHED
Object MOOG
MOOG is a well-known automotive parts brand specializing in high-quality steering and suspension components.
E1348848 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: MOOG | Statement: [Federal-Mogul, brand, MOOG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: MOOG
Context triple: [Federal-Mogul, brand, MOOG]
  • A. Bendix
    Bendix is a surname most notably associated with American actor William Bendix, known for his roles in mid-20th-century film and radio.
  • B. Pennock
    Pennock is the surname of Herb Pennock, a Hall of Fame American Major League Baseball pitcher best known for his years with the New York Yankees in the 1920s.
  • C. Mogami
    Mogami was a lead ship of a class of Japanese World War II heavy cruisers known for their high speed, heavy armament, and participation in major Pacific naval battles.
  • D. Eaton
    Eaton is the namesake of the Eaton Professor of the Science of Government at Harvard University, an endowed academic chair in political science and government studies.
  • E. Eaton
    Eaton is a small town located within Madison County in the state of New York, 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: MOOG
Triple: [Federal-Mogul, brand, MOOG]
Generated description
MOOG is a well-known automotive parts brand specializing in high-quality steering and suspension components.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: MOOG
Target entity description: MOOG is a well-known automotive parts brand specializing in high-quality steering and suspension components.
  • A. Bendix
    Bendix is a surname most notably associated with American actor William Bendix, known for his roles in mid-20th-century film and radio.
  • B. Pennock
    Pennock is the surname of Herb Pennock, a Hall of Fame American Major League Baseball pitcher best known for his years with the New York Yankees in the 1920s.
  • C. Mogami
    Mogami was a lead ship of a class of Japanese World War II heavy cruisers known for their high speed, heavy armament, and participation in major Pacific naval battles.
  • D. Eaton
    Eaton is a surname most notably associated with American decathlete and Olympic gold medalist Ashton Eaton.
  • E. Eaton
    Eaton is the namesake of the Eaton Professor of the Science of Government at Harvard University, an endowed academic chair in political science and government studies.
  • 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_69d8dcfd05bc819088903cca13cc2846 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5c52cc9cc8190ac489d36e51693c8 completed April 20, 2026, 6:18 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0582bf4f9481908098c8d405225553 completed May 14, 2026, 8:07 a.m.
NEDg Description generation batch_6a05899f7a008190a9b0324f403f09ac completed May 14, 2026, 8:36 a.m.
NED2 Entity disambiguation (via description) batch_6a058a1028288190b9fddb9b6b123651 completed May 14, 2026, 8:38 a.m.
Created at: April 10, 2026, 11:58 a.m.