Triple

T7578259
Position Surface form Disambiguated ID Type / Status
Subject John Galt E179415 entity
Predicate employer P7 FINISHED
Object Canada Company E179416 NE FINISHED

How this triple was built (2 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: Canada Company | Statement: [John Galt, employer, Canada Company]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Canada Company
Context triple: [John Galt, employer, Canada Company]
  • A. Canada Company chosen
    Canada Company was a 19th-century British land development firm that played a major role in colonizing and settling parts of Upper Canada (now Ontario).
  • B. John Company
    John Company is a historical nickname for the British East India Company, the powerful trading corporation that played a central role in establishing British rule in India.
  • C. United Company
    United Company was a prominent late 17th-century London theatre company formed by the merger of the King’s Company and the Duke’s Company, active during the Restoration period.
  • D. Martin Company
    Martin Company was a major American aerospace and defense contractor known for developing missiles, spacecraft, and military systems before merging into Lockheed Martin.
  • E. Marcus Corporation
    Marcus Corporation is a U.S.-based company best known for its movie theatre and hospitality businesses, including operating cinema chains and hotels.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69c69f327db881909a21ae3b156f8ded completed March 27, 2026, 3:16 p.m.
NER Named-entity recognition batch_69c6f97460a481909d61fba555567b66 completed March 27, 2026, 9:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8616d87f881909fe23220dc77167c completed March 28, 2026, 11:17 p.m.
Created at: March 27, 2026, 3:51 p.m.