Triple

T11472533
Position Surface form Disambiguated ID Type / Status
Subject Say Yes to the Dress E271943 entity
Predicate settingLocation P40 FINISHED
Object Manhattan E8787 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: Manhattan | Statement: [Say Yes to the Dress, settingLocation, Manhattan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Manhattan
Context triple: [Say Yes to the Dress, settingLocation, Manhattan]
  • A. Manhattan chosen
    Manhattan is the densely populated, iconic core borough of New York City, known for its skyscrapers, cultural institutions, and role as a global financial and media center.
  • B. 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.
  • C. New York City
    New York City is the largest city in the United States, a global center of finance, culture, media, and technology.
  • D. NYC
    NYC is a historic American railroad company that operated major passenger and freight services across the northeastern and midwestern United States.
  • E. Brooklyn
    Brooklyn is a populous and culturally diverse borough of New York City known for its distinct neighborhoods, arts scene, and iconic landmarks like the Brooklyn Bridge.
  • 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_69d6aae0c8d881908a5a360c0be3242e completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8294b3f388190a587c358313f7260 completed April 9, 2026, 10:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69e5e8b19b1c8190bc9147a9fc73e35b completed April 20, 2026, 8:49 a.m.
Created at: April 8, 2026, 9:35 p.m.