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.