Triple
T24699636
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boomerang Coast to Coaster |
E611694
|
entity |
| Predicate | typicalTrainCars |
P42282
|
FINISHED |
| Object | 7 or more cars per train |
—
|
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: 7 or more cars per train | Statement: [Boomerang Coast to Coaster, typicalTrainCars, 7 or more cars per train]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalTrainCars Context triple: [Boomerang Coast to Coaster, typicalTrainCars, 7 or more cars per train]
-
A.
vehiclesPerTrain
chosen
Indicates the number of vehicles that are attached to or make up a single train.
-
B.
railCarries
Indicates that a rail or railway system transports or conveys a specified entity from one place to another.
-
C.
trainsCategory
Indicates that one entity is a category or type under which the other entity is trained or classified.
-
D.
trains
Indicates that one entity teaches, instructs, or coaches another entity to develop skills, knowledge, or abilities.
-
E.
trainTypeUsed
Indicates that a specific type or category of train is employed or operated in a given context 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_69e2c4d76d148190b58ad612467149a5 |
completed | April 17, 2026, 11:40 p.m. |
| NER | Named-entity recognition | batch_69f606c79ad081908369605f72e65ca6 |
completed | May 2, 2026, 2:14 p.m. |
| PD | Predicate disambiguation | batch_69f602ce79ec8190b8336c2b9de18ac7 |
completed | May 2, 2026, 1:57 p.m. |
Created at: April 18, 2026, 3:22 a.m.