Triple
T11399421
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | China High-Speed Rail Network |
E270066
|
entity |
| Predicate | usesTrainType |
P56947
|
FINISHED |
| Object | electric multiple unit |
—
|
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: electric multiple unit | Statement: [China High-Speed Rail Network, usesTrainType, electric multiple unit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesTrainType Context triple: [China High-Speed Rail Network, usesTrainType, electric multiple unit]
-
A.
trainTypeUsed
chosen
Indicates that a specific type or category of train is employed or operated in a given context or service.
-
B.
usesTrainNumber
Indicates that one entity operates, identifies, or references another entity by a specific train number.
-
C.
hasRailMode
Indicates that an entity is associated with or supports transportation via rail-based modes (such as trains, trams, or subways).
-
D.
railServiceType
Indicates the specific category or type of rail service that applies to the relationship between the involved entities (e.g., local, express, freight).
-
E.
maintainsTrainsFor
Indicates that one entity is responsible for servicing, repairing, or otherwise keeping trains operational for 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_69d6aacdbc6c8190af6dc3d5f5d22836 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d8001adc188190ae45227856156412 |
completed | April 9, 2026, 7:38 p.m. |
| PD | Predicate disambiguation | batch_69d7e70b228c8190b87f5101fd683788 |
completed | April 9, 2026, 5:51 p.m. |
Created at: April 8, 2026, 9:34 p.m.