Triple

T33858020
Position Surface form Disambiguated ID Type / Status
Subject Claremore Public Schools E867840 entity
Predicate hasSchool P113 FINISHED
Object Roosa Elementary School
Roosa Elementary School is a public elementary school serving early-grade students in the Claremore Public Schools district in Claremore, Oklahoma.
E2071442 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: Roosa Elementary School | Statement: [Claremore Public Schools, hasSchool, Roosa 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: Roosa Elementary School
Triple: [Claremore Public Schools, hasSchool, Roosa Elementary School]
Generated description
Roosa Elementary School is a public elementary school serving early-grade students in the Claremore Public Schools district in Claremore, Oklahoma.

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_69f349943ccc8190a3c41a3e0ae46cbf completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f7007970ac819092f5ab972587afd3 completed May 3, 2026, 7:59 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36761bc94c8190ab367c2aff9562a2 completed June 20, 2026, 11:14 a.m.
NEDg Description generation batch_6a3676e442208190b316373c4b23df03 completed June 20, 2026, 11:17 a.m.
NED2 Entity disambiguation (via description) batch_6a3677b8a3748190895cb5ccd2f90f6b completed June 20, 2026, 11:21 a.m.
Created at: May 1, 2026, 1:47 a.m.