Triple

T34822048
Position Surface form Disambiguated ID Type / Status
Subject Greybull, Wyoming E1003805 entity
Predicate hasEducationalInstitution P113 FINISHED
Object Greybull Elementary School
Greybull Elementary School is a primary education institution serving young students in the small town of Greybull, Wyoming.
E2117927 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: Greybull Elementary School | Statement: [Greybull, Wyoming, hasEducationalInstitution, Greybull 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: Greybull Elementary School
Triple: [Greybull, Wyoming, hasEducationalInstitution, Greybull Elementary School]
Generated description
Greybull Elementary School is a primary education institution serving young students in the small town of Greybull, Wyoming.

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_69f76db717088190811b4e744610f37d completed May 3, 2026, 3:45 p.m.
NER Named-entity recognition batch_69f77adeac048190bbe22f1b663009d5 completed May 3, 2026, 4:42 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3786c82d0481908e30f004638c78ca completed June 21, 2026, 6:38 a.m.
NEDg Description generation batch_6a37917d7d008190b9319b9d653070c6 completed June 21, 2026, 7:23 a.m.
NED2 Entity disambiguation (via description) batch_6a379216d1348190b20b1a852b9c2676 completed June 21, 2026, 7:26 a.m.
Created at: May 3, 2026, 4 p.m.