Triple
T760278
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Namba Station |
E16052
|
entity |
| Predicate | adjacentTo |
P224
|
FINISHED |
| Object | Shinsaibashi |
E19641
|
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: Shinsaibashi | Statement: [Namba Station, adjacentTo, Shinsaibashi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shinsaibashi Context triple: [Namba Station, adjacentTo, Shinsaibashi]
-
A.
Shinsaibashi
chosen
Shinsaibashi is a major shopping and entertainment district in central Osaka, Japan, known for its covered arcade, fashion boutiques, and vibrant nightlife.
-
B.
Motomachi
Motomachi is a historic commercial and shopping district in Kobe, Japan, known for its fashionable boutiques, cafes, and proximity to the city’s Chinatown and waterfront.
-
C.
Motomachi
Motomachi is a stylish shopping and entertainment district in Yokohama known for its fashionable boutiques, cafes, and Western-influenced atmosphere.
-
D.
Dotonbori
Dotonbori is a famous entertainment and nightlife district in Osaka, Japan, known for its neon billboards, street food, and canal-side atmosphere.
-
E.
Umeda district
Umeda district is a major commercial and transportation hub in Osaka, Japan, known for its skyscrapers, shopping complexes, and extensive train and subway connections.
- 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_69a493684ee48190bd43b7c78da4aec8 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a68158508190998da10e69252662 |
completed | March 1, 2026, 8:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad15866b448190b20334eddca756eb |
completed | March 8, 2026, 6:21 a.m. |
Created at: March 1, 2026, 7:37 p.m.