Triple

T5599716
Position Surface form Disambiguated ID Type / Status
Subject 神戸大学 E147086 entity
Predicate hasFaculty P141 FINISHED
Object 農学部
農学部は、農業・生物資源・環境などに関する教育と研究を行う大学の学部です。
 E528902 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: 農学部 | Statement: [神戸大学, hasFaculty, 農学部]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: 農学部
Context triple: [神戸大学, hasFaculty, 農学部]
  • A. 農学研究科
    農学研究科は、東北大学に設置された農学分野の高度な教育・研究を行う大学院研究科です。
  • B. School of Resources and Environmental Science
    The School of Resources and Environmental Science is a specialized academic unit of Wuhan University focused on education and research in natural resources management, environmental science, and related interdisciplinary fields.
  • C. Beijing University of Agriculture
    Beijing University of Agriculture is a specialized higher education institution in Beijing focusing on agricultural sciences, technology, and related applied disciplines.
  • D. Faculty of Agriculture
    The Faculty of Agriculture is an academic division of South Valley University specializing in agricultural sciences, research, and education related to farming, crops, and rural development.
  • E. Faculty of Agriculture
    The Faculty of Agriculture at Sohag University is an academic division specializing in agricultural sciences, education, and research to support regional and national agricultural development.
  • 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: 農学部
Triple: [神戸大学, hasFaculty, 農学部]
Generated description
農学部は、農業・生物資源・環境などに関する教育と研究を行う大学の学部です。
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: 農学部
Target entity description: 農学部は、農業・生物資源・環境などに関する教育と研究を行う大学の学部です。
  • A. 農学研究科
    農学研究科は、東北大学に設置された農学分野の高度な教育・研究を行う大学院研究科です。
  • B. School of Resources and Environmental Science
    The School of Resources and Environmental Science is a specialized academic unit of Wuhan University focused on education and research in natural resources management, environmental science, and related interdisciplinary fields.
  • C. Beijing University of Agriculture
    Beijing University of Agriculture is a specialized higher education institution in Beijing focusing on agricultural sciences, technology, and related applied disciplines.
  • D. Faculty of Agriculture
    The Faculty of Agriculture is an academic division of South Valley University specializing in agricultural sciences, research, and education related to farming, crops, and rural development.
  • E. Faculty of Agriculture
    The Faculty of Agriculture at Sohag University is an academic division specializing in agricultural sciences, education, and research to support regional and national agricultural development.
  • 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_69c009043d648190a7af89698ccf1e3e completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c020d936dc8190a2e599f1df9fdd91 completed March 22, 2026, 5:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0287139508190aa646918228cfdc0 completed March 22, 2026, 5:35 p.m.
NEDg Description generation batch_69c0350eb53081909dc573fefa3e7f0a completed March 22, 2026, 6:29 p.m.
NED2 Entity disambiguation (via description) batch_69c036ee4e1c8190b9e60655d72407ff completed March 22, 2026, 6:37 p.m.
Created at: March 22, 2026, 3:38 p.m.