Triple
T11087191
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shivering Timbers |
E262150
|
entity |
| Predicate | hasTrainCarsPerTrain |
P42282
|
FINISHED |
| Object | several 2-bench 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: several 2-bench cars per train | Statement: [Shivering Timbers, hasTrainCarsPerTrain, several 2-bench cars per train]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTrainCarsPerTrain Context triple: [Shivering Timbers, hasTrainCarsPerTrain, several 2-bench cars per train]
-
A.
vehiclesPerTrain
chosen
Indicates the number of vehicles that are attached to or make up a single train.
-
B.
numberOfPowerCars
Indicates the relationship specifying how many power cars (self-propelled units) are associated with or contained in a given train or rail consist.
-
C.
railCarries
Indicates that a rail or railway system transports or conveys a specified entity from one place to another.
-
D.
rowsPerCar
Indicates the number of rows associated with or allocated to each individual car.
-
E.
numberOfPassengerCars
Indicates the total count of passenger cars associated with or contained in a given entity or context.
- 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_69d6aa9983c08190b0ef61603b69feac |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d799c5008081908f59612243fa4f7a |
completed | April 9, 2026, 12:21 p.m. |
| PD | Predicate disambiguation | batch_69d744185a5881909ba4cf151d1798ec |
completed | April 9, 2026, 6:15 a.m. |
Created at: April 8, 2026, 9:27 p.m.