Triple

T34476802
Position Surface form Disambiguated ID Type / Status
Subject Chiryū E885059 entity
Predicate hasShrine P15258 FINISHED
Object Chiryū Jinja
Chiryū Jinja is a historic Shinto shrine in Chiryū, Aichi Prefecture, known for its cultural significance and traditional festivals.
E2289485 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: Chiryū Jinja | Statement: [Chiryū, hasShrine, Chiryū Jinja]
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: Chiryū Jinja
Triple: [Chiryū, hasShrine, Chiryū Jinja]
Generated description
Chiryū Jinja is a historic Shinto shrine in Chiryū, Aichi Prefecture, known for its cultural significance and traditional festivals.

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_69f349c880408190ade571c471ab154a completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f71ccbb6f081909f8107528622022c completed May 3, 2026, 10 a.m.
NED1 Entity disambiguation (via context triple) batch_6a5b425656248190bac0b8c3600364d3 completed July 18, 2026, 9:07 a.m.
NEDg Description generation batch_6a5b42d10ae0819085c4a7b16e6f94f2 completed July 18, 2026, 9:09 a.m.
NED2 Entity disambiguation (via description) batch_6a5b4306fe58819095a7fe7e30ec146f completed July 18, 2026, 9:10 a.m.
Created at: May 1, 2026, 2:01 a.m.