Triple

T9891654
Position Surface form Disambiguated ID Type / Status
Subject Department of Public Health, Faculty of Medicine, The University of Tokyo E181462 entity
Predicate alsoKnownAs P39 FINISHED
Object Department of Public Health, University of Tokyo Faculty of Medicine
The Department of Public Health, University of Tokyo Faculty of Medicine is an academic and research department specializing in public health science, epidemiology, and health policy within Japan’s leading medical school.
E183993 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: Department of Public Health, University of Tokyo Faculty of Medicine | Statement: [Department of Public Health, Faculty of Medicine, The University of Tokyo, alsoKnownAs, Department of Public Health, University of Tokyo Faculty of Medicine]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Department of Public Health, University of Tokyo Faculty of Medicine
Context triple: [Department of Public Health, Faculty of Medicine, The University of Tokyo, alsoKnownAs, Department of Public Health, University of Tokyo Faculty of Medicine]
  • A. Department of Public Health, Kyoto University
    The Department of Public Health at Kyoto University is an academic and research unit specializing in population health, epidemiology, and preventive medicine within the university’s Faculty of Medicine.
  • B. Faculty of Medicine, The University of Tokyo
    The Faculty of Medicine at the University of Tokyo is a leading Japanese medical school and research institution renowned for training physicians and advancing biomedical science.
  • C. Institute of Medical Science, University of Tokyo
    The Institute of Medical Science, University of Tokyo is a leading Japanese biomedical research and graduate education center renowned for its work in infectious diseases, genomics, and advanced medical science.
  • D. Graduate School of Medicine, Tohoku University
    The Graduate School of Medicine, Tohoku University is a leading Japanese medical graduate institution known for advanced research and education in clinical medicine, biomedical sciences, and public health.
  • E. School of Health Sciences, Kyushu University
    The School of Health Sciences at Kyushu University is an academic faculty specializing in education and research in health and medical sciences within one of Japan’s leading national universities.
  • 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: Department of Public Health, University of Tokyo Faculty of Medicine
Triple: [Department of Public Health, Faculty of Medicine, The University of Tokyo, alsoKnownAs, Department of Public Health, University of Tokyo Faculty of Medicine]
Generated description
The Department of Public Health, University of Tokyo Faculty of Medicine is an academic and research department specializing in public health science, epidemiology, and health policy within Japan’s leading medical school.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Department of Public Health, University of Tokyo Faculty of Medicine
Target entity description: The Department of Public Health, University of Tokyo Faculty of Medicine is an academic and research department specializing in public health science, epidemiology, and health policy within Japan’s leading medical school.
  • A. Department of Public Health, Kyoto University
    The Department of Public Health at Kyoto University is an academic and research unit specializing in population health, epidemiology, and preventive medicine within the university’s Faculty of Medicine.
  • B. Faculty of Medicine, The University of Tokyo chosen
    The Faculty of Medicine at the University of Tokyo is a leading Japanese medical school and research institution renowned for training physicians and advancing biomedical science.
  • C. Institute of Medical Science, University of Tokyo
    The Institute of Medical Science, University of Tokyo is a leading Japanese biomedical research and graduate education center renowned for its work in infectious diseases, genomics, and advanced medical science.
  • D. Graduate School of Medicine, Tohoku University
    The Graduate School of Medicine, Tohoku University is a leading Japanese medical graduate institution known for advanced research and education in clinical medicine, biomedical sciences, and public health.
  • E. School of Health Sciences, Kyushu University
    The School of Health Sciences at Kyushu University is an academic faculty specializing in education and research in health and medical sciences within one of Japan’s leading national universities.
  • F. None of above.

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_69ca8283a6708190801af7a25a7ebb9f completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cdb47f69588190924a078ea88c97ef completed April 2, 2026, 12:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1eb08075c81908e017df8048daba8 completed April 5, 2026, 4:54 a.m.
NEDg Description generation batch_69d1eca8703c8190a473fdafa2a2d273 completed April 5, 2026, 5:01 a.m.
NED2 Entity disambiguation (via description) batch_69d1ed2a1318819087a5b787724fa10c completed April 5, 2026, 5:03 a.m.
Created at: March 30, 2026, 8:39 p.m.