Triple
T14495153
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chūō-ku, Fukuoka |
E359475
|
entity |
| Predicate | romanization |
P2508
|
FINISHED |
| Object | Chūō-ku |
—
|
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: Chūō-ku | Statement: [Chūō-ku, Fukuoka, romanization, Chūō-ku]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chūō-ku Context triple: [Chūō-ku, Fukuoka, romanization, Chūō-ku]
-
A.
Chūō-ku
Chūō-ku is a central ward of Osaka, Japan, known as a major commercial and entertainment hub featuring famous landmarks, shopping streets, and nightlife areas.
-
B.
Chūō-ku
chosen
Chūō-ku is a central ward of Fukuoka City in Japan, known as a major commercial, entertainment, and administrative hub.
-
C.
Chūō-ku
Chūō-ku is a central ward of Tokyo, Japan, known as a major commercial and business district that includes areas like Ginza and Nihonbashi.
-
D.
Bunkyō-ku
Bunkyō-ku is a central Tokyo ward known for its universities, cultural institutions, and quiet residential neighborhoods.
-
E.
Shimogyo-ku
Shimogyo-ku is a central ward of Kyoto, Japan, known for its major commercial areas, transportation hubs, and historic sites.
- 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_69d8279740308190af9df93a3af8592e |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de93109cb081909a6e846db23a4635 |
completed | April 14, 2026, 7:18 p.m. |
Created at: April 10, 2026, 1:21 a.m.