Triple
T19056998
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vande Bharat Express |
E466422
|
entity |
| Predicate | trainSetConfiguration |
P96474
|
FINISHED |
| Object | multiple-unit trainset |
—
|
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-unit trainset | Statement: [Vande Bharat Express, trainSetConfiguration, multiple-unit trainset]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainSetConfiguration Context triple: [Vande Bharat Express, trainSetConfiguration, multiple-unit trainset]
-
A.
trainsetComposition
chosen
Indicates the relationship specifying how a trainset is composed from its constituent vehicles or units.
-
B.
trainControl
Indicates that one entity exercises authority over or manages the operation, direction, or behavior of another entity in a training or instructional context.
-
C.
trainingSetSize
Indicates the number of examples or instances included in a dataset used to train a model or system.
-
D.
trainsetType
Indicates the specific category or role of a dataset within a training process (e.g., training, validation, or test set).
-
E.
trainFeature
Indicates that an entity is a characteristic, capability, or attribute associated with a train.
- 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_69d8dd040fb881909af2a964f65ad208 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5dc067f788190b3b149dfee370435 |
completed | April 20, 2026, 7:55 a.m. |
| PD | Predicate disambiguation | batch_69e4b99633c8819097988608c278ecf8 |
completed | April 19, 2026, 11:16 a.m. |
Created at: April 10, 2026, 12:03 p.m.