Triple
T3890367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TGV Atlantique trainset |
E88045
|
entity |
| Predicate | trainsetConfiguration |
P26476
|
FINISHED |
| Object | articulated |
—
|
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: articulated | Statement: [TGV Atlantique trainset, trainsetConfiguration, articulated]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainsetConfiguration Context triple: [TGV Atlantique trainset, trainsetConfiguration, articulated]
-
A.
trainControl
Indicates that one entity exercises authority over or manages the operation, direction, or behavior of another entity in a training or instructional context.
-
B.
trainingUse
Indicates that something is used for training purposes, such as preparing, educating, or improving the skills or performance of an entity.
-
C.
trainingDataSource
Indicates the origin or provider from which the training data for a model or system is obtained.
-
D.
trainingSetSize
Indicates the number of examples or instances included in a dataset used to train a model or system.
-
E.
trainConfiguration
chosen
Indicates the specific arrangement and composition of train elements (such as locomotives and cars) used together for a particular operation or service.
- 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_69aed9466d548190939f5217a23ed4ac |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeecb0ba448190aa076865b7762002 |
completed | March 9, 2026, 3:52 p.m. |
| PD | Predicate disambiguation | batch_69aee759609c8190985e96ec6d96dedd |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:21 p.m.