Triple
T4733096
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | JR Namba Station |
E105057
|
entity |
| Predicate | serves |
P98
|
FINISHED |
| Object | Namba district |
E162524
|
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: Namba district | Statement: [JR Namba Station, serves, Namba district]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Namba district Context triple: [JR Namba Station, serves, Namba district]
-
A.
Namba district
chosen
Namba district is a major entertainment and shopping area in Osaka, Japan, known for its neon lights, bustling nightlife, and iconic landmarks.
-
B.
Kanda district
Kanda district is a historic commercial and cultural area in central Tokyo known for its old bookstores, electronics shops, and traditional shrines.
-
C.
Tenma district
Tenma district is a bustling urban neighborhood in Osaka, Japan, known for its traditional shopping arcades, lively nightlife, and historic Tenmangu Shrine.
-
D.
Shimen District
Shimen District is a rural coastal district in northern Taiwan known for its scenic shoreline, historic sites, and role as part of New Taipei City.
-
E.
Nishinakajima district
Nishinakajima district is an urban neighborhood in Osaka, Japan, known for its convenient access to central Osaka via nearby subway and railway 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_69bd43ee52048190b81a4f066534ffb3 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6466354481908595f5bb56025cdb |
completed | March 20, 2026, 3:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be5c8a20a88190a668251abbc1c7c8 |
completed | March 21, 2026, 8:53 a.m. |
Created at: March 20, 2026, 1:19 p.m.