Triple

T11748141
Position Surface form Disambiguated ID Type / Status
Subject Sylvia Woods E279335 entity
Predicate locationOfBusiness P40 FINISHED
Object New York E550 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: New York | Statement: [Sylvia Woods, locationOfBusiness, New York]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: New York
Context triple: [Sylvia Woods, locationOfBusiness, New York]
  • A. New York chosen
    New York is a populous and economically significant U.S. state known for New York City, a global center of finance, culture, and media.
  • B. New York City
    New York City is the largest city in the United States, a global center of finance, culture, media, and technology.
  • C. Washington, New York
    Washington, New York is a rural town in Dutchess County known for its historic hamlet of Millbrook, scenic landscapes, and equestrian and agricultural heritage.
  • D. NYC
    NYC is a historic American railroad company that operated major passenger and freight services across the northeastern and midwestern United States.
  • E. Manhattan
    The Manhattan is a classic whiskey-based cocktail, traditionally made with rye or bourbon, sweet vermouth, and bitters, and typically served stirred and garnished with a cherry.
  • 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_69d6ab01038c819080714901502c84fc completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a50763a081908597da118bd0a64e completed April 10, 2026, 7:21 a.m.
NED1 Entity disambiguation (via context triple) batch_69f457f7be0081908f8e1760cc7b8294 completed May 1, 2026, 7:36 a.m.
Created at: April 8, 2026, 9:41 p.m.