Triple

T20986919
Position Surface form Disambiguated ID Type / Status
Subject Lafayette Street E516913 entity
Predicate passesThrough P225 FINISHED
Object NoHo NE NERFINISHED

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: NoHo | Statement: [Lafayette Street, passesThrough, NoHo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: NoHo
Context triple: [Lafayette Street, passesThrough, NoHo]
  • A. NoHo
    NoHo is a small, upscale neighborhood in Lower Manhattan known for its historic cast-iron architecture, converted lofts, and vibrant arts and dining scene.
  • B. NoHo
    NoHo is a vibrant arts and entertainment district in the North Hollywood neighborhood of Los Angeles known for its theaters, galleries, and creative community.
  • C. SoHo
    SoHo is a vibrant commercial and entertainment district in Hong Kong known for its trendy restaurants, bars, and nightlife.
  • D. SoHo
    SoHo is a fashionable Lower Manhattan neighborhood known for its cast-iron architecture, art galleries, and upscale boutiques.
  • E. NoHo, Manhattan chosen
    NoHo, Manhattan is a small, upscale neighborhood in Lower Manhattan known for its historic cast-iron architecture, trendy boutiques, and vibrant arts and dining scene.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4ffac148190bbade9f0eceb660b completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6fbe3fbac819086d3079aaddca5b1 completed April 21, 2026, 4:24 a.m.
Created at: April 16, 2026, 1:49 p.m.