Triple
T11134262
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sapporo Municipal Subway |
E263370
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Sapporo Station |
E496276
|
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: Sapporo Station | Statement: [Sapporo Municipal Subway, hasStation, Sapporo Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sapporo Station Context triple: [Sapporo Municipal Subway, hasStation, Sapporo Station]
-
A.
Sapporo Station
chosen
Sapporo Station is the main railway hub of Sapporo, Japan, serving as a major transit, commercial, and business center in Hokkaido.
-
B.
Nagano Station
Nagano Station is a major railway hub in Nagano, Japan, serving as a gateway to the region’s ski resorts, temples, and surrounding mountain areas.
-
C.
Sendai Station
Sendai Station is the main railway hub of Sendai, Japan, serving as a major stop for Shinkansen high-speed trains and numerous local and regional lines.
-
D.
Yokohama Station
Yokohama Station is one of Japan’s busiest railway hubs, serving numerous JR, private, and subway lines in central Yokohama.
-
E.
Tenjin Station
Tenjin Station is a major underground railway and commercial hub in central Fukuoka, Japan, serving as one of the city's busiest transit and shopping areas.
- 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_69d6aa9c0ba08190bbd19c217489b755 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8347a248190837e8c26f25f553a |
completed | April 9, 2026, 5:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00cfb725d48190bdca0a85ca7f440c |
completed | May 10, 2026, 6:34 p.m. |
Created at: April 8, 2026, 9:28 p.m.