Triple
T1453267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Osaka Metro Sennichimae Line |
E31340
|
entity |
| Predicate | hasLocale |
P387
|
FINISHED |
| Object | Chūō-ku, Osaka |
E260288
|
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: Chūō-ku, Osaka | Statement: [Osaka Metro Sennichimae Line, hasLocale, Chūō-ku, Osaka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chūō-ku, Osaka Context triple: [Osaka Metro Sennichimae Line, hasLocale, Chūō-ku, Osaka]
-
A.
Chuo-ku, Osaka
chosen
Chuo-ku, Osaka is a central ward of Osaka City known as a major commercial, business, and entertainment hub.
-
B.
Minato-ku, Osaka
Minato-ku, Osaka is a coastal ward of Osaka City known for its waterfront attractions, including major entertainment, shopping, and port facilities.
-
C.
Yodogawa-ku, Osaka
Yodogawa-ku, Osaka is a ward in northern Osaka City known as a major transportation hub, notably hosting Shin-Osaka Station, the city’s primary Shinkansen terminal.
-
D.
Konohana-ku, Osaka
Konohana-ku, Osaka is a ward of Osaka City in Japan known for hosting major attractions like Universal Studios Japan and its themed entertainment areas.
-
E.
Naniwa-ku, Osaka
Naniwa-ku, Osaka is a central ward of Osaka City known for its busy commercial districts, entertainment areas, and major transport hubs such as Namba.
- 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_69a499171a28819085b993a3ac78e363 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c57e82d48190a30a4512f39f5de0 |
completed | March 1, 2026, 11:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1f83bf9248190821580e4d1e76e4d |
completed | March 11, 2026, 11:18 p.m. |
Created at: March 1, 2026, 8 p.m.