Triple
T5913926
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bombardier Innovia APM 200 |
E131530
|
entity |
| Predicate | typicalTrainFormation |
P42282
|
FINISHED |
| Object | multiple-car trainsets |
—
|
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: multiple-car trainsets | Statement: [Bombardier Innovia APM 200, typicalTrainFormation, multiple-car trainsets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalTrainFormation Context triple: [Bombardier Innovia APM 200, typicalTrainFormation, multiple-car trainsets]
-
A.
trainConfiguration
Indicates the specific arrangement and composition of train elements (such as locomotives and cars) used together for a particular operation or service.
-
B.
vehiclesPerTrain
chosen
Indicates the number of vehicles that are attached to or make up a single train.
-
C.
trainTypeUsed
Indicates that a specific type or category of train is employed or operated in a given context or service.
-
D.
trains
Indicates that one entity teaches, instructs, or coaches another entity to develop skills, knowledge, or abilities.
-
E.
trainsOn
Indicates that one entity receives training, instruction, or practice using or based on another entity (such as a resource, dataset, tool, or 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_69c008593a44819081a07ae0efe6c574 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c048fc112c8190b905bf561c9de096 |
completed | March 22, 2026, 7:54 p.m. |
| PD | Predicate disambiguation | batch_69c03352208c8190968efed05a9fd416 |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 3:59 p.m.