Triple
T11876931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | うめだスカイビル |
E282551
|
entity |
| Predicate | 最寄り駅 |
P90655
|
FINISHED |
| Object | 大阪駅 |
—
|
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: 大阪駅 | Statement: [うめだスカイビル, 最寄り駅, 大阪駅]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 最寄り駅 Context triple: [うめだスカイビル, 最寄り駅, 大阪駅]
-
A.
nearestShinkansenStation
Indicates that one entity is the Shinkansen (bullet train) station geographically closest to the other entity.
-
B.
nearestRailwayTerminus
Indicates that one location is the closest railway terminus to another specified place.
-
C.
nearestSuburbanRailwayStation
chosen
Indicates the relationship where a given place is associated with the suburban railway station that is geographically closest to it.
-
D.
operatorOfNearestStation
Indicates that an entity is the organization or operator responsible for managing the station that is geographically closest to a given reference point or entity.
-
E.
nearestEntranceStation
Indicates that one station is the closest entrance station to a given location or entity compared to all other candidate stations.
- 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_69d6ab2945d081908a5851c916cbcfb5 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8d39d2934819093b9f7006f45e5cb |
completed | April 10, 2026, 10:40 a.m. |
| PD | Predicate disambiguation | batch_69d8bb272f88819090c37c944c5a60ab |
completed | April 10, 2026, 8:56 a.m. |
Created at: April 8, 2026, 9:44 p.m.