Triple

T34729110
Position Surface form Disambiguated ID Type / Status
Subject Kinokuniya bookshop (Shinjuku South store) E1001157 entity
Predicate partOf P40 FINISHED
Object Kinokuniya bookstore chain
Kinokuniya bookstore chain is a major Japanese bookseller known for its large, multi-floor stores and extensive selection of Japanese and international books, magazines, and media, with branches across Japan and overseas.
E2110153 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: Kinokuniya bookstore chain | Statement: [Kinokuniya bookshop (Shinjuku South store), partOf, Kinokuniya bookstore chain]
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: Kinokuniya bookstore chain
Triple: [Kinokuniya bookshop (Shinjuku South store), partOf, Kinokuniya bookstore chain]
Generated description
Kinokuniya bookstore chain is a major Japanese bookseller known for its large, multi-floor stores and extensive selection of Japanese and international books, magazines, and media, with branches across Japan and overseas.

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_69f76daeb6e48190a4c9a6b0edc80f72 completed May 3, 2026, 3:45 p.m.
NER Named-entity recognition batch_69f779aaa91c8190a49d6fa51ebbb510 completed May 3, 2026, 4:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a375beb79148190ab3801252894d369 completed June 21, 2026, 3:35 a.m.
NEDg Description generation batch_6a375d8be710819093766d7c9b7fc5dd completed June 21, 2026, 3:42 a.m.
NED2 Entity disambiguation (via description) batch_6a375e54876c819090b0073c6ded34ec completed June 21, 2026, 3:45 a.m.
Created at: May 3, 2026, 3:59 p.m.