Triple

T13282410
Position Surface form Disambiguated ID Type / Status
Subject Lebanon, New Hampshire E316355 entity
Predicate hasMajorEmployer P588 FINISHED
Object Timken
Timken is a global industrial manufacturer best known for its engineered bearings and power transmission products used across automotive, aerospace, and heavy industry sectors.
 E681815 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: Timken | Statement: [Lebanon, New Hampshire, hasMajorEmployer, Timken]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Timken
Context triple: [Lebanon, New Hampshire, hasMajorEmployer, Timken]
  • A. Meritor
    Meritor is the abbreviated name of Meritor Savings Bank, FSB, a former U.S. financial institution that provided consumer and commercial banking services.
  • B. SKF
    SKF is a Swedish multinational engineering company best known as one of the world’s leading manufacturers of bearings and related industrial technologies.
  • C. Eaton’s
    Eaton’s was a major Canadian department store chain that became a retail icon and helped shape downtown shopping districts across the country.
  • D. Eaton
    Eaton is a small town located within Madison County in the state of New York, United States.
  • E. Eaton
    Eaton is a surname most notably associated with American decathlete and Olympic gold medalist Ashton Eaton.
  • 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: Timken
Triple: [Lebanon, New Hampshire, hasMajorEmployer, Timken]
Generated description
Timken is a global industrial manufacturer best known for its engineered bearings and power transmission products used across automotive, aerospace, and heavy industry sectors.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Timken
Target entity description: Timken is a global industrial manufacturer best known for its engineered bearings and power transmission products used across automotive, aerospace, and heavy industry sectors.
  • A. Meritor
    Meritor is the abbreviated name of Meritor Savings Bank, FSB, a former U.S. financial institution that provided consumer and commercial banking services.
  • B. SKF chosen
    SKF is a Swedish multinational engineering company best known as one of the world’s leading manufacturers of bearings and related industrial technologies.
  • C. Eaton’s
    Eaton’s was a major Canadian department store chain that became a retail icon and helped shape downtown shopping districts across the country.
  • D. Eaton
    Eaton is a small town located within Madison County in the state of New York, United States.
  • E. Eaton
    Eaton is a surname most notably associated with American decathlete and Olympic gold medalist Ashton Eaton.
  • F. None of above.

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_69d806b349908190a9a61dd9323bf153 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d9904507588190a303686d176ec3e1 completed April 11, 2026, 12:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69f70a592d748190b55129515f71e979 completed May 3, 2026, 8:42 a.m.
NEDg Description generation batch_69f70b117c588190bb81ff53664cac4a completed May 3, 2026, 8:45 a.m.
NED2 Entity disambiguation (via description) batch_69f70c04da34819091e01db25741674e completed May 3, 2026, 8:49 a.m.
Created at: April 9, 2026, 9:27 p.m.