Triple

T9894
Position Surface form Disambiguated ID Type / Status
Subject Newton, Massachusetts E200 entity
Predicate hasPart P35 FINISHED
Object Newton Corner E200 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: Newton Corner | Statement: [Newton, Massachusetts, hasPart, Newton Corner]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Newton Corner
Context triple: [Newton, Massachusetts, hasPart, Newton Corner]
  • A. Newton chosen
    Newton is a suburban city in Massachusetts known for its residential neighborhoods, strong public schools, and proximity to Boston.
  • B. John Harvard
    John Harvard was a 17th-century English clergyman and benefactor whose substantial bequest helped establish the institution that became Harvard University.
  • C. John Hancock
    John Hancock was a prominent American Founding Father, revolutionary leader, and the first signer of the United States Declaration of Independence, known for his bold and iconic signature.
  • D. Pierre Charles L’Enfant
    Pierre Charles L’Enfant was a French-born American architect and civil engineer best known for creating the original plan for the city of Washington, D.C.
  • E. Harold Hazen
    Harold Hazen was an American electrical engineer and MIT professor known for his pioneering work in control systems and his role in developing early analog computing devices.
  • 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_69a23bb612708190b09f25385e4b63d1 completed Feb. 28, 2026, 12:49 a.m.
NER Named-entity recognition batch_69a23ff2f0508190806663ab2463cd41 completed Feb. 28, 2026, 1:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69a243c87d988190a9d0649c4a04c7b7 completed Feb. 28, 2026, 1:24 a.m.
Created at: Feb. 28, 2026, 12:54 a.m.