Triple
T9272113
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shizuo Kambayashi |
E222853
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Shizuo |
E222853
|
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: Shizuo | Statement: [Shizuo Kambayashi, givenName, Shizuo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shizuo Context triple: [Shizuo Kambayashi, givenName, Shizuo]
-
A.
Shizuo
chosen
Shizuo is a Japanese masculine given name borne by several notable figures, including mathematicians and artists.
-
B.
Shinpei
Shinpei is a Japanese given name commonly used for males and borne by various notable figures in politics, arts, and entertainment.
-
C.
Shintaro
Shintaro is a Japanese given name commonly used for males and borne by various notable figures in sports, entertainment, and politics.
-
D.
Kenjirō
Kenjirō is a Japanese masculine given name that can be written with various kanji combinations and is borne by multiple notable individuals in fields such as sports, arts, and entertainment.
-
E.
Ryoji
Ryoji is a Japanese given name commonly used for males.
- 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_69ca841ffe208190aa7bcffbef2f8379 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd0787d2e48190ad56cf3abf12d409 |
completed | April 1, 2026, 11:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d74fa545b8819092809c605542476a |
completed | April 9, 2026, 7:05 a.m. |
Created at: March 30, 2026, 7:33 p.m.