Triple
T4109011
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TGV Réseau |
E88522
|
entity |
| Predicate | numberOfPassengerCars |
P54685
|
FINISHED |
| Object | 8 |
—
|
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: 8 | Statement: [TGV Réseau, numberOfPassengerCars, 8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPassengerCars Context triple: [TGV Réseau, numberOfPassengerCars, 8]
-
A.
numberOfVehicles
Indicates the total count of vehicles associated with a given entity or context.
-
B.
numberOfCarsPerUnit
Indicates the quantity of cars associated with each single unit of a specified measure (such as time, distance, or entity).
-
C.
numberOfPowerCars
Indicates the relationship specifying how many power cars (self-propelled units) are associated with or contained in a given train or rail consist.
-
D.
rowsPerCar
Indicates the number of rows associated with or allocated to each individual car.
-
E.
numberOfRidersPerVehicle
Indicates the quantity of riders associated with each individual vehicle in the relationship.
- F. None of above. chosen
Provenance (4 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_69aed9484fb881909146f4c772ad277c |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af03d7240c8190a64dcbc669772808 |
completed | March 9, 2026, 5:31 p.m. |
| PD | Predicate disambiguation | batch_69af0183eb84819087d7184de28f5514 |
completed | March 9, 2026, 5:21 p.m. |
| PDg | Predicate description generation | batch_69af03d5cce88190ad53bc6cfe10b21e |
completed | March 9, 2026, 5:31 p.m. |
Created at: March 9, 2026, 3:40 p.m.