Triple
T15619402
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shinsekai |
E375508
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Naniwa Ward |
—
|
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: Naniwa Ward | Statement: [Shinsekai, locatedIn, Naniwa Ward]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Naniwa Ward Context triple: [Shinsekai, locatedIn, Naniwa Ward]
-
A.
Naniwa Ward
chosen
Naniwa Ward is a central district of Osaka, Japan, known for its bustling commercial areas, entertainment districts, and historical sites.
-
B.
Suminoe Ward
Suminoe Ward is one of Osaka City's 24 wards, known for its coastal location, residential districts, and industrial and port-related facilities.
-
C.
Adachi Ward
Adachi Ward is a special ward in northern Tokyo, Japan, known for its residential neighborhoods, riverside areas, and role as a commuter hub within the Tokyo metropolitan area.
-
D.
Arakawa Ward
Arakawa Ward is a special ward in northeastern Tokyo, Japan, known for its mix of residential neighborhoods, traditional shitamachi atmosphere, and riverside areas.
-
E.
Koto Ward
Koto Ward is a special ward in eastern Tokyo, Japan, known for its waterfront areas, canals, and mix of residential, commercial, and industrial districts.
- 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_69d85ccf2794819096cda4cbcb02d478 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e997ce481909b2f10d25705fbc6 |
completed | April 16, 2026, 2:51 a.m. |
Created at: April 10, 2026, 4:13 a.m.