Triple

T8213379
Position Surface form Disambiguated ID Type / Status
Subject Uniform Land Use Review Procedure E191875 entity
Predicate abbreviation P43 FINISHED
Object ULURP
ULURP is New York City’s formal public review process for evaluating and approving changes to land use, zoning, and development proposals.
 E718867 NE FINISHED

How this triple was built (4 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: ULURP | Statement: [Uniform Land Use Review Procedure, abbreviation, ULURP]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ULURP
Context triple: [Uniform Land Use Review Procedure, abbreviation, ULURP]
  • A. URS
    URS was the FIFA country code used to represent the Soviet Union national football team in international competitions.
  • B. URU
    URU is the FIFA country code used to represent the Uruguay national football team in international competitions and rankings.
  • C. UNMEER
    UNMEER (United Nations Mission for Ebola Emergency Response) was a UN emergency health mission created to coordinate and accelerate international efforts to contain and end the West Africa Ebola outbreak.
  • D. UL
    UL is the vehicle registration code used on license plates for the city of Ulm in Germany.
  • E. UL
    UL is the New York Stock Exchange ticker symbol for Unilever, a major multinational consumer goods company known for its food, personal care, and household products.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: ULURP
Triple: [Uniform Land Use Review Procedure, abbreviation, ULURP]
Generated description
ULURP is New York City’s formal public review process for evaluating and approving changes to land use, zoning, and development proposals.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ULURP
Target entity description: ULURP is New York City’s formal public review process for evaluating and approving changes to land use, zoning, and development proposals.
  • A. URS
    URS was the FIFA country code used to represent the Soviet Union national football team in international competitions.
  • B. URU
    URU is the FIFA country code used to represent the Uruguay national football team in international competitions and rankings.
  • C. UNMEER
    UNMEER (United Nations Mission for Ebola Emergency Response) was a UN emergency health mission created to coordinate and accelerate international efforts to contain and end the West Africa Ebola outbreak.
  • D. UL
    UL is the vehicle registration code used on license plates for the city of Ulm in Germany.
  • E. UL
    UL is the New York Stock Exchange ticker symbol for Unilever, a major multinational consumer goods company known for its food, personal care, and household products.
  • F. None of above. chosen

Provenance (5 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_69ca82c8c054819087fedd9a5436b8a3 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb76e1ffa081908883338e7d3a7c6d completed March 31, 2026, 7:25 a.m.
NED1 Entity disambiguation (via context triple) batch_69ccedea881481909f9348778290eb63 completed April 1, 2026, 10:05 a.m.
NEDg Description generation batch_69ccf1ba74548190831677bb126bea1a completed April 1, 2026, 10:21 a.m.
NED2 Entity disambiguation (via description) batch_69cd05d668ac819098195ba5ec26ec76 completed April 1, 2026, 11:47 a.m.
Created at: March 30, 2026, 5:44 p.m.