Triple

T33586720
Position Surface form Disambiguated ID Type / Status
Subject Borough Hall station E860302 entity
Predicate adjacentTo P224 FINISHED
Object Nevins Street station
Nevins Street station is a New York City Subway complex in Downtown Brooklyn serving multiple lines and providing a key transfer point between services.
 E2072197 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: Nevins Street station | Statement: [Borough Hall station, adjacentTo, Nevins Street station]
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: Nevins Street station
Triple: [Borough Hall station, adjacentTo, Nevins Street station]
Generated description
Nevins Street station is a New York City Subway complex in Downtown Brooklyn serving multiple lines and providing a key transfer point between services.

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_69f3497e70e48190951c94d072879bec completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6f774f2f88190b8673017cce0c287 completed May 3, 2026, 7:21 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3675fc8a248190a9b8245457a09c34 completed June 20, 2026, 11:14 a.m.
NEDg Description generation batch_6a3678b3e0048190942d4ff227339498 completed June 20, 2026, 11:25 a.m.
NED2 Entity disambiguation (via description) batch_6a367930695c8190a4e925fe114aaf0f completed June 20, 2026, 11:27 a.m.
Created at: May 1, 2026, 1:40 a.m.