Triple

T35295237
Position Surface form Disambiguated ID Type / Status
Subject Clarence Center, New York E1019342 entity
Predicate hasCommunityFacility P4719 FINISHED
Object Clarence Center Elementary School
Clarence Center Elementary School is a public primary school serving the local community in Clarence Center, New York.
E2136137 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: Clarence Center Elementary School | Statement: [Clarence Center, New York, hasCommunityFacility, Clarence Center Elementary School]
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: Clarence Center Elementary School
Triple: [Clarence Center, New York, hasCommunityFacility, Clarence Center Elementary School]
Generated description
Clarence Center Elementary School is a public primary school serving the local community in Clarence Center, 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_69f76de7eedc8190a3bdc64ebbc05b42 completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f7901ccb748190bb39013b50761c01 completed May 3, 2026, 6:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3823bb5d388190a93f5fd2b82e9e10 completed June 21, 2026, 5:47 p.m.
NEDg Description generation batch_6a382467c4dc8190825a1698150af8f0 completed June 21, 2026, 5:50 p.m.
NED2 Entity disambiguation (via description) batch_6a3825338a88819090c8dfafb2dc5e42 completed June 21, 2026, 5:53 p.m.
Created at: May 3, 2026, 4:03 p.m.