Triple
T653735
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nagoya University |
E11599
|
entity |
| Predicate | hasNobelLaureate |
P324
|
FINISHED |
| Object | Hiroshi Amano |
E59837
|
NE FINISHED |
How this triple was built (2 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: Hiroshi Amano | Statement: [Nagoya University, hasNobelLaureate, Hiroshi Amano]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hiroshi Amano Context triple: [Nagoya University, hasNobelLaureate, Hiroshi Amano]
-
A.
Hiroshi Amano
chosen
Hiroshi Amano is a Japanese physicist and Nobel laureate renowned for his pioneering work on blue light-emitting diodes (LEDs) and semiconductor technology.
-
B.
Tadahiko Mibuchi
Tadahiko Mibuchi was a Japanese jurist who became the first person to serve as Chief Justice of Japan.
-
C.
Yoshihisa Hirano
Yoshihisa Hirano is a Japanese professional baseball pitcher known for his successful career in Nippon Professional Baseball and Major League Baseball, particularly as a late-inning reliever.
-
D.
Hirofumi Hirano
Hirofumi Hirano is a Japanese politician who has held senior leadership roles in major opposition parties and served in the national legislature.
-
E.
Shigeo Hirose
Shigeo Hirose is a pioneering Japanese roboticist renowned for his innovative work in robot mechanisms and design, particularly in snake-like and walking robots.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a4932862a0819098be659c814e4981 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a517ac148190aa032b77885bf709 |
completed | March 1, 2026, 8:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac2573c6a48190bfe9b7f2ec026462 |
completed | March 7, 2026, 1:17 p.m. |
Created at: March 1, 2026, 7:36 p.m.