Triple

T28819384
Position Surface form Disambiguated ID Type / Status
Subject Paintsville, Kentucky E727720 entity
Predicate hasEducationalInstitution P113 FINISHED
Object Paintsville High School
Paintsville High School is a public secondary school serving students in the small city of Paintsville in eastern Kentucky.
E1836737 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: Paintsville High School | Statement: [Paintsville, Kentucky, hasEducationalInstitution, Paintsville High 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: Paintsville High School
Triple: [Paintsville, Kentucky, hasEducationalInstitution, Paintsville High School]
Generated description
Paintsville High School is a public secondary school serving students in the small city of Paintsville in eastern Kentucky.

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_69f0319d09088190bbf14cdf1987792a completed April 28, 2026, 4:03 a.m.
NER Named-entity recognition batch_69f658f653948190978153d28f5e772c completed May 2, 2026, 8:05 p.m.
NED1 Entity disambiguation (via context triple) batch_6a24bba3019881909301076655dcee48 completed June 7, 2026, 12:30 a.m.
NEDg Description generation batch_6a24bfb0987c819089a6705bb5282c4e completed June 7, 2026, 12:47 a.m.
NED2 Entity disambiguation (via description) batch_6a24c54a5d648190b33935999d8f5f04 completed June 7, 2026, 1:11 a.m.
Created at: April 28, 2026, 6:34 a.m.