Triple

T27393027
Position Surface form Disambiguated ID Type / Status
Subject Westbury, New York E691590 entity
Predicate adjacentTo P224 FINISHED
Object Salisbury, New York
Salisbury, New York is a suburban hamlet in Nassau County on Long Island, known primarily as a residential community near major commercial and transportation hubs.
 E1885608 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: Salisbury, New York | Statement: [Westbury, New York, adjacentTo, Salisbury, New York]
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: Salisbury, New York
Triple: [Westbury, New York, adjacentTo, Salisbury, New York]
Generated description
Salisbury, New York is a suburban hamlet in Nassau County on Long Island, known primarily as a residential community near major commercial and transportation hubs.

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_69ef520386788190bc92cfcd97ebb67a completed April 27, 2026, 12:09 p.m.
NER Named-entity recognition batch_69f62cae21408190836baa6f4a1b52a2 completed May 2, 2026, 4:56 p.m.
NED1 Entity disambiguation (via context triple) batch_6a26e5c7324481908d7727a6d2b54002 completed June 8, 2026, 3:54 p.m.
NEDg Description generation batch_6a26e6776f9481908df0bc905c664756 completed June 8, 2026, 3:57 p.m.
NED2 Entity disambiguation (via description) batch_6a26e7abb57c819095ad0e1dbf8a9be8 completed June 8, 2026, 4:02 p.m.
Created at: April 27, 2026, 12:26 p.m.