Triple
T16887397
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Masaoka Shiki |
E421573
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Noboru |
—
|
NE NERFINISHED |
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: Noboru | Statement: [Masaoka Shiki, givenName, Noboru]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Noboru Context triple: [Masaoka Shiki, givenName, Noboru]
-
A.
Noboru
chosen
Noboru is a Japanese masculine given name commonly borne by notable figures in politics, sports, and entertainment.
-
B.
Yasuhiko
Yasuhiko is a Japanese given name notably borne by Prince Asaka Yasuhiko, a member of the Imperial Family of Japan in the early 20th century.
-
C.
Kuniaki
Kuniaki is a Japanese masculine given name that can be written with various kanji combinations and has been borne by several notable figures, including politicians and athletes.
-
D.
Hironori
Hironori is a larger composite entity or system of which Hiro is a constituent part.
-
E.
Tadahiko
Tadahiko is a Japanese masculine given name used by various notable individuals in fields such as sports, arts, and academia.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889d470fc8190b4aec199636c0c56 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e3bbc1f42481909dcf595358c23497 |
completed | April 18, 2026, 5:13 p.m. |
Created at: April 10, 2026, 5:29 a.m.