Triple
T13692062
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sakura |
E328289
|
entity |
| Predicate | usesTrainFormation |
P56947
|
FINISHED |
| Object | 8-car N700 series sets |
—
|
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: 8-car N700 series sets | Statement: [Sakura, usesTrainFormation, 8-car N700 series sets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesTrainFormation Context triple: [Sakura, usesTrainFormation, 8-car N700 series sets]
-
A.
maintainsTrainsFor
Indicates that one entity is responsible for servicing, repairing, or otherwise keeping trains operational for another entity.
-
B.
trainTypeUsed
chosen
Indicates that a specific type or category of train is employed or operated in a given context or service.
-
C.
usesRollingStock
Indicates that one entity employs or operates specific rolling stock (such as rail vehicles) in its activities or services.
-
D.
usesTrainNumber
Indicates that one entity operates, identifies, or references another entity by a specific train number.
-
E.
usesRollingStockCompatibleWith
Indicates that one entity operates using rolling stock that is technically and operationally compatible with the rolling stock standards or systems associated with another entity.
- 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_69d8076ff62081908a7bd79889edd7a0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc8746458819095ec1ba3c01ef31b |
completed | April 12, 2026, 4:29 p.m. |
| PD | Predicate disambiguation | batch_69dbbe9059488190a8113177c83e1481 |
completed | April 12, 2026, 3:47 p.m. |
Created at: April 9, 2026, 9:53 p.m.