Triple

T37350560
Position Surface form Disambiguated ID Type / Status
Subject Pennsylvania Avenue, Brooklyn E927309 entity
Predicate connectsTo P845 FINISHED
Object Seaview Avenue, Brooklyn
Seaview Avenue in Brooklyn is a major east–west thoroughfare in the borough’s southeastern neighborhoods, running through areas such as Canarsie and East New York.
E2228206 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: Seaview Avenue, Brooklyn | Statement: [Pennsylvania Avenue, Brooklyn, connectsTo, Seaview Avenue, Brooklyn]
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: Seaview Avenue, Brooklyn
Triple: [Pennsylvania Avenue, Brooklyn, connectsTo, Seaview Avenue, Brooklyn]
Generated description
Seaview Avenue in Brooklyn is a major east–west thoroughfare in the borough’s southeastern neighborhoods, running through areas such as Canarsie and East New York.

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_69f76eb5e034819088e53ab5b7909a68 completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fb5bc062e8819098f3b5486eb754d1 completed May 6, 2026, 3:18 p.m.
NED1 Entity disambiguation (via context triple) batch_6a408c20fd28819094bd0a2530fdaabc completed June 28, 2026, 2:51 a.m.
NEDg Description generation batch_6a408d41efa48190a0d89da42e673c2b completed June 28, 2026, 2:56 a.m.
NED2 Entity disambiguation (via description) batch_6a408dab83008190b966064e782ca385 completed June 28, 2026, 2:57 a.m.
Created at: May 3, 2026, 4:16 p.m.