Triple
T34849158
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tokyo–Shin-Osaka |
E1004552
|
entity |
| Predicate | hasEndpointStationRole |
P34947
|
FINISHED |
| Object | Tokyo Station terminus |
—
|
LITERAL 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: Tokyo Station terminus | Statement: [Tokyo–Shin-Osaka, hasEndpointStationRole, Tokyo Station terminus]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEndpointStationRole Context triple: [Tokyo–Shin-Osaka, hasEndpointStationRole, Tokyo Station terminus]
-
A.
hasEndpointStation
chosen
Indicates that something (such as a route, line, or service) has a specific station as one of its terminal endpoints.
-
B.
servesAsThroughStationFor
Indicates that a station functions as an intermediate (through) stop for a particular service, route, or journey rather than as its starting or ending point.
-
C.
hasStationCodeRole
Indicates that an entity holds or is assigned a specific role associated with a station code within a system or context.
-
D.
hasInterchangeStationWith
Indicates that two transportation lines, routes, or systems share a station where passengers can transfer between them.
-
E.
isPassengerStationFor
Indicates that a station serves as a boarding and alighting point for passengers on a particular transportation line or service.
- F. None of above.
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_69f76dba76f0819090643cba102c41ec |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fd553d7cb881908d243e7a9f30ac85 |
completed | May 8, 2026, 3:15 a.m. |
| PD | Predicate disambiguation | batch_69fd514dcb1c81908333c70d7edd79c9 |
completed | May 8, 2026, 2:58 a.m. |
Created at: May 3, 2026, 4 p.m.