Triple
T7265713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 0 Series Shinkansen |
E159767
|
entity |
| Predicate | numberOfCarsBuilt |
P2091
|
FINISHED |
| Object | over 3000 cars |
—
|
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: over 3000 cars | Statement: [0 Series Shinkansen, numberOfCarsBuilt, over 3000 cars]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCarsBuilt Context triple: [0 Series Shinkansen, numberOfCarsBuilt, over 3000 cars]
-
A.
numberBuilt
chosen
Indicates the total count of items or structures that have been constructed or produced.
-
B.
numberOfCarsPerUnit
Indicates the quantity of cars associated with each single unit of a specified measure (such as time, distance, or entity).
-
C.
numberOfPassengerCars
Indicates the total count of passenger cars associated with or contained in a given entity or context.
-
D.
numberOfVehicles
Indicates the total count of vehicles associated with a given entity or context.
-
E.
numberOfCarsPerSet
Indicates the quantity of cars that are included within a single set.
- 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_69c68838f9948190875fd60b2351230c |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6eb088dac8190b353f6ea3d686025 |
completed | March 27, 2026, 8:39 p.m. |
| PD | Predicate disambiguation | batch_69c6e76876608190ac4652bc7153302e |
completed | March 27, 2026, 8:24 p.m. |
Created at: March 27, 2026, 2:57 p.m.