Triple
T24479973
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shinkansen lines |
E617343
|
entity |
| Predicate | notableTrainType |
P56947
|
FINISHED |
| Object | Nozomi |
—
|
NE NERFINISHED |
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: Nozomi | Statement: [Shinkansen lines, notableTrainType, Nozomi]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableTrainType Context triple: [Shinkansen lines, notableTrainType, Nozomi]
-
A.
notableTrain
Indicates that there is a train or rail service associated with the subject that is considered notable or significant in some way.
-
B.
notableTrainBrand
Indicates that an entity is a well-known or significant brand associated with trains or railway services.
-
C.
trainTypeUsed
chosen
Indicates that a specific type or category of train is employed or operated in a given context or service.
-
D.
trainsCategory
Indicates that one entity is a category or type under which the other entity is trained or classified.
-
E.
notableRollingStock
Indicates that there is a notable or historically significant piece of rolling stock (such as a train car or locomotive) associated with the subject.
- 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_69e2d7f3ae788190b683394db15f220e |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f29ed509c88190a0071f8e78b38887 |
completed | April 30, 2026, 12:14 a.m. |
| PD | Predicate disambiguation | batch_69f287d76c7c81909494f12e606a9149 |
completed | April 29, 2026, 10:36 p.m. |
Created at: April 18, 2026, 2:21 a.m.