Triple
T19182052
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shinsaibashi Station |
E469597
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Amerikamura |
—
|
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: Amerikamura | Statement: [Shinsaibashi Station, near, Amerikamura]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amerikamura Context triple: [Shinsaibashi Station, near, Amerikamura]
-
A.
Amerikamura
chosen
Amerikamura is a trendy youth-oriented district in Osaka known for its American-influenced fashion, street culture, and vibrant nightlife.
-
B.
Munchi
Munchi is an alternative name for the Tiv language, a Southern Bantoid language spoken primarily in central Nigeria.
-
C.
Munchi
Munchi is a Dutch music producer and DJ known for pioneering the modern moombahton sound by blending reggaeton, house, and various global bass influences.
-
D.
Amerikamura area
Amerikamura area is a trendy youth culture and shopping district in Osaka known for its fashion boutiques, street art, and vibrant nightlife.
-
E.
The American
The American is a historical novel by Howard Fast that dramatizes the life and political struggles of labor leader William "Big Bill" Haywood in early 20th-century America.
- 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_69d8dd09d5a081909ae43c286651ae5a |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5f61cab348190965e96ac0f701f9f |
completed | April 20, 2026, 9:47 a.m. |
Created at: April 10, 2026, 12:07 p.m.