Triple

T2454894
Position Surface form Disambiguated ID Type / Status
Subject 枚方市 E54395 entity
Predicate 大学 P21900 FINISHED
Object 摂南大学
摂南大学は、大阪府に本部を置き、薬学部や理工学部などを擁する私立総合大学です。
 E267809 NE FINISHED

How this triple was built (4 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: 摂南大学 | Statement: [枚方市, 大学, 摂南大学]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: 摂南大学
Context triple: [枚方市, 大学, 摂南大学]
  • A. Kansai Gaidai University
    Kansai Gaidai University is a private Japanese university renowned for its programs in foreign languages, international studies, and study-abroad opportunities.
  • B. Seikei University
    Seikei University is a private Japanese university in Tokyo known for educating several prominent political and business leaders, including former Prime Minister Shinzo Abe.
  • C. Nagoya City University
    Nagoya City University is a public university in Nagoya, Japan, known for its programs in medicine, pharmaceutical sciences, design, and humanities.
  • D. Niigata University
    Niigata University is a national research university in Niigata, Japan, known for its comprehensive programs across humanities, sciences, engineering, and medical fields.
  • E. University of Shiga Prefecture
    The University of Shiga Prefecture is a Japanese public university known for its focus on environmental science, engineering, and regional studies in the Lake Biwa area.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: 摂南大学
Triple: [枚方市, 大学, 摂南大学]
Generated description
摂南大学は、大阪府に本部を置き、薬学部や理工学部などを擁する私立総合大学です。
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: 摂南大学
Target entity description: 摂南大学は、大阪府に本部を置き、薬学部や理工学部などを擁する私立総合大学です。
  • A. Kansai Gaidai University
    Kansai Gaidai University is a private Japanese university renowned for its programs in foreign languages, international studies, and study-abroad opportunities.
  • B. Seikei University
    Seikei University is a private Japanese university in Tokyo known for educating several prominent political and business leaders, including former Prime Minister Shinzo Abe.
  • C. Nagoya City University
    Nagoya City University is a public university in Nagoya, Japan, known for its programs in medicine, pharmaceutical sciences, design, and humanities.
  • D. Niigata University
    Niigata University is a national research university in Niigata, Japan, known for its comprehensive programs across humanities, sciences, engineering, and medical fields.
  • E. University of Shiga Prefecture
    The University of Shiga Prefecture is a Japanese public university known for its focus on environmental science, engineering, and regional studies in the Lake Biwa area.
  • F. None of above. chosen

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_69ab49dee84c819096b50a0049c347ac completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd82f2020819086bbd321a750ce43 completed March 7, 2026, 7:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69aef0c7c90c8190ba5ed3cece5e049f completed March 9, 2026, 4:09 p.m.
NEDg Description generation batch_69aef5ca95dc8190b0f7f20d2128ae93 completed March 9, 2026, 4:31 p.m.
NED2 Entity disambiguation (via description) batch_69aef632e2e08190b21023cbb0f12be8 completed March 9, 2026, 4:32 p.m.
Created at: March 6, 2026, 9:44 p.m.