Triple

T28130126
Position Surface form Disambiguated ID Type / Status
Subject Tokyo University of the Arts E711041 entity
Predicate hasFaculty P141 FINISHED
Object Faculty of Music
The Faculty of Music at Tokyo University of the Arts is a leading Japanese institution for advanced music education, performance, and research.
 E711043 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: Faculty of Music | Statement: [Tokyo University of the Arts, hasFaculty, Faculty of Music]
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: Faculty of Music
Triple: [Tokyo University of the Arts, hasFaculty, Faculty of Music]
Generated description
The Faculty of Music at Tokyo University of the Arts is a leading Japanese institution for advanced music education, performance, and research.

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_69ef9b73bd288190a21ae3d6aa14f386 completed April 27, 2026, 5:22 p.m.
NER Named-entity recognition batch_69f640ff720c8190a6edb7f6b5dd5b7e completed May 2, 2026, 6:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a15d79c76a481908ff523eb4ea54035 completed May 26, 2026, 5:25 p.m.
NEDg Description generation batch_6a15d99437988190afef98b9bc8700b9 completed May 26, 2026, 5:34 p.m.
NED2 Entity disambiguation (via description) batch_6a15da4ba6188190a3b29508bc40baa9 completed May 26, 2026, 5:37 p.m.
Created at: April 27, 2026, 9:22 p.m.