Triple
T6735442
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tanaka |
E153742
|
entity |
| Predicate | romanizedAs |
P2508
|
FINISHED |
| Object | Tanaka |
E153742
|
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: Tanaka | Statement: [Tanaka, romanizedAs, Tanaka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tanaka Context triple: [Tanaka, romanizedAs, Tanaka]
-
A.
Tanaka
chosen
Tanaka is a common Japanese surname borne by numerous notable figures in politics, arts, sports, and other fields.
-
B.
Takamado
Takamado is a Japanese imperial family name most prominently associated with the late Prince Takamado and his descendants, a branch of Japan’s royal household.
-
C.
Takaishi
Takaishi is a city in Osaka Prefecture, Japan, known as a small industrial and residential hub within the Osaka metropolitan area.
-
D.
Nishiwaki
Nishiwaki is a city in central Hyōgo Prefecture, Japan, known for its location near the geographic center of the country and its mix of industrial and rural landscapes.
-
E.
Kiyokawa
Kiyokawa is a small rural village in Kanagawa Prefecture, Japan, known for its mountainous scenery and outdoor recreation.
- 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_69c6880bdd68819097de8b6099992682 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d16ecbe08190b019d547f631a725 |
completed | March 27, 2026, 6:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8c7aa218081908cba76a4fdaa9f10 |
completed | March 29, 2026, 6:33 a.m. |
Created at: March 27, 2026, 2:09 p.m.