Triple

T27849409
Position Surface form Disambiguated ID Type / Status
Subject 東京大学 駒場キャンパス E703908 entity
Predicate hasFacility P105 FINISHED
Object 駒場博物館
駒場博物館は、東京大学駒場キャンパス内に位置し、主に同大学が所蔵する学術資料や美術品などを公開・展示する大学附属博物館です。
 E1792835 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: 駒場博物館 | Statement: [東京大学 駒場キャンパス, hasFacility, 駒場博物館]
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: 駒場博物館
Triple: [東京大学 駒場キャンパス, hasFacility, 駒場博物館]
Generated description
駒場博物館は、東京大学駒場キャンパス内に位置し、主に同大学が所蔵する学術資料や美術品などを公開・展示する大学附属博物館です。

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_69ef840d9e3c819093615ebff4ec22be completed April 27, 2026, 3:43 p.m.
NER Named-entity recognition batch_69f639040e748190a283658f38d24ef7 completed May 2, 2026, 5:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12f73bbb688190b26d9a718a494201 completed May 24, 2026, 1:03 p.m.
NEDg Description generation batch_6a12fb4a4a808190bc0821b2bc754da0 completed May 24, 2026, 1:21 p.m.
NED2 Entity disambiguation (via description) batch_6a12fd21fb2c8190b52459bd901c05a0 completed May 24, 2026, 1:29 p.m.
Created at: April 27, 2026, 6:09 p.m.