Triple

T32705132
Position Surface form Disambiguated ID Type / Status
Subject Okazaki E836252 entity
Predicate hasEducationalInstitution P113 FINISHED
Object Aichi Gakusen University
Aichi Gakusen University is a Japanese higher education institution located in Okazaki, Aichi Prefecture, offering undergraduate and possibly graduate programs across various academic fields.
E2289191 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: Aichi Gakusen University | Statement: [Okazaki, hasEducationalInstitution, Aichi Gakusen University]
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: Aichi Gakusen University
Triple: [Okazaki, hasEducationalInstitution, Aichi Gakusen University]
Generated description
Aichi Gakusen University is a Japanese higher education institution located in Okazaki, Aichi Prefecture, offering undergraduate and possibly graduate programs across various academic fields.

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_69f3493446148190819541f3ffe79975 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6c850ea5881909f5e24e12a07c439 completed May 3, 2026, 4 a.m.
NED1 Entity disambiguation (via context triple) batch_6a5b1046a0448190919756ea30ce83b0 completed July 18, 2026, 5:33 a.m.
NEDg Description generation batch_6a5b1123a2948190869c84426bdaf17a completed July 18, 2026, 5:37 a.m.
NED2 Entity disambiguation (via description) batch_6a5b1193de548190b076cf1324b53677 completed July 18, 2026, 5:39 a.m.
Created at: May 1, 2026, 1:10 a.m.