Triple

T32500548
Position Surface form Disambiguated ID Type / Status
Subject Central District, Seattle E830644 entity
Predicate hasPublicSchool P113 FINISHED
Object Bailey Gatzert Elementary School
Bailey Gatzert Elementary School is a public elementary school in Seattle, Washington, serving students in the city’s Central District neighborhood.
E2010584 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: Bailey Gatzert Elementary School | Statement: [Central District, Seattle, hasPublicSchool, Bailey Gatzert 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: Bailey Gatzert Elementary School
Triple: [Central District, Seattle, hasPublicSchool, Bailey Gatzert Elementary School]
Generated description
Bailey Gatzert Elementary School is a public elementary school in Seattle, Washington, serving students in the city’s Central District neighborhood.

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_69f349219cb8819087e120f509629c1b completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6c442b4d88190a00e9781206b0520 completed May 3, 2026, 3:42 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34705a55008190b5cf60cab8aa145b completed June 18, 2026, 10:25 p.m.
NEDg Description generation batch_6a3471350ec08190ae5394b2a8028840 completed June 18, 2026, 10:29 p.m.
NED2 Entity disambiguation (via description) batch_6a3471ce69508190bbd47938ea429317 completed June 18, 2026, 10:31 p.m.
Created at: May 1, 2026, 12:59 a.m.