Triple
T12085889
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juntendo University |
E287805
|
entity |
| Predicate | founder |
P104
|
FINISHED |
| Object |
Taizen Sato
Taizen Sato was a Japanese physician and educator best known for establishing Juntendo University, one of Japan’s oldest medical institutions.
|
E988586
|
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: Taizen Sato | Statement: [Juntendo University, founder, Taizen Sato]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Taizen Sato Context triple: [Juntendo University, founder, Taizen Sato]
-
A.
Koji Sato
Koji Sato is a Japanese automotive executive who serves as the president and CEO of Toyota Motor Corporation.
-
B.
Shun Satō
Shun Satō is a Japanese figure skater known for competing internationally in men's singles events.
-
C.
Makoto Uchida
Makoto Uchida is a Japanese automotive executive who serves as the chief executive officer of Nissan Motor Co.
-
D.
Sawao Kato
Sawao Kato is a legendary Japanese artistic gymnast, renowned for winning multiple Olympic gold medals in the 1960s and 1970s and being considered one of the greatest male gymnasts in history.
-
E.
Hyakutake Yuji
Hyakutake Yuji is a Japanese amateur astronomer best known for discovering Comet Hyakutake, one of the brightest and most widely observed comets of the 20th century.
- 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: Taizen Sato Triple: [Juntendo University, founder, Taizen Sato]
Generated description
Taizen Sato was a Japanese physician and educator best known for establishing Juntendo University, one of Japan’s oldest medical institutions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Taizen Sato Target entity description: Taizen Sato was a Japanese physician and educator best known for establishing Juntendo University, one of Japan’s oldest medical institutions.
-
A.
Koji Sato
Koji Sato is a Japanese automotive executive who serves as the president and CEO of Toyota Motor Corporation.
-
B.
Shun Satō
Shun Satō is a Japanese figure skater known for competing internationally in men's singles events.
-
C.
Makoto Uchida
Makoto Uchida is a Japanese automotive executive who serves as the chief executive officer of Nissan Motor Co.
-
D.
Sawao Kato
Sawao Kato is a legendary Japanese artistic gymnast, renowned for winning multiple Olympic gold medals in the 1960s and 1970s and being considered one of the greatest male gymnasts in history.
-
E.
Hyakutake Yuji
Hyakutake Yuji is a Japanese amateur astronomer best known for discovering Comet Hyakutake, one of the brightest and most widely observed comets of the 20th century.
- 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_69d6ab4964708190850585628b287b0c |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d91514c78c8190bc1cd569e524e8b4 |
completed | April 10, 2026, 3:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f655551d8c81909bf0980951f10320 |
completed | May 2, 2026, 7:49 p.m. |
| NEDg | Description generation | batch_69f6566dccc0819085e059c7b0288f6c |
completed | May 2, 2026, 7:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f657aec8fc8190b3b08ccb95595958 |
completed | May 2, 2026, 7:59 p.m. |
Created at: April 8, 2026, 9:48 p.m.