Triple
T15951911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TGV V150 |
E386837
|
entity |
| Predicate | numberOfDoubleDeckCoaches |
P121094
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [TGV V150, numberOfDoubleDeckCoaches, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfDoubleDeckCoaches Context triple: [TGV V150, numberOfDoubleDeckCoaches, 3]
-
A.
numberOfCoaches
Indicates the total count of coaches associated with a given entity or context.
-
B.
numberOfIntermediateCoaches
Indicates the count of intermediate coaches (cars) that exist between two specified endpoints in a train configuration.
-
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.
vehiclesPerTrain
Indicates the number of vehicles that are attached to or make up a single train.
-
E.
numberOfDecks
Indicates the quantity of decks associated with or contained in an entity.
- 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_69d86da882448190a82ea962fe343b79 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e17d4d08f481909f38b75e3f42d9ab |
completed | April 17, 2026, 12:22 a.m. |
| PD | Predicate disambiguation | batch_69e142d37cd88190ab50760f1783e20c |
completed | April 16, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69e17d48cc9c8190b03fd07ae2e9dfd8 |
completed | April 17, 2026, 12:22 a.m. |
Created at: April 10, 2026, 4:53 a.m.