Triple
T12660947
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daisuke |
E302421
|
entity |
| Predicate | canBeRomanizedAs |
P2508
|
FINISHED |
| Object | Daisuke |
E302421
|
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: Daisuke | Statement: [Daisuke, canBeRomanizedAs, Daisuke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daisuke Context triple: [Daisuke, canBeRomanizedAs, Daisuke]
-
A.
Daisuke
chosen
Daisuke is a common Japanese masculine given name used by various notable figures in entertainment, sports, and other fields.
-
B.
Taisuke
Taisuke is a Japanese given name notably borne by historical figures such as the Meiji-era politician Itagaki Taisuke.
-
C.
Takahito
Takahito, better known by his title Prince Mikasa, was a member of the Japanese imperial family and the youngest son of Emperor Taishō.
-
D.
Takehiro
Takehiro is a central character in Ryūnosuke Akutagawa’s short story “In a Grove,” whose ambiguous fate is revealed through conflicting eyewitness testimonies.
-
E.
Akinobu
Akinobu is a Japanese masculine given name that can be written with various kanji combinations and is borne by several notable individuals.
- 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_69d7bded71a88190bb76e2413af9ea66 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9617b07ec8190b714f04ae6654060 |
completed | April 10, 2026, 8:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6a539f098819096e955a035742dad |
completed | May 3, 2026, 1:30 a.m. |
Created at: April 9, 2026, 5:19 p.m.