Triple
T34530348
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas Rocket |
E886518
|
entity |
| Predicate | trainStyle |
P142501
|
FINISHED |
| Object | streamlined train |
—
|
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: streamlined train | Statement: [Texas Rocket, trainStyle, streamlined train]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainStyle Context triple: [Texas Rocket, trainStyle, streamlined train]
-
A.
hasTrainStyle
chosen
Indicates that one entity (typically a train or rail service) is characterized by or associated with a particular style, type, or configuration of train.
-
B.
trainCategory
Indicates the classification or type of a train within a railway system (e.g., express, regional, freight).
-
C.
trainsetType
Indicates the specific category or role of a dataset within a training process (e.g., training, validation, or test set).
-
D.
trainTypeUsed
Indicates that a specific type or category of train is employed or operated in a given context or service.
-
E.
railwayStationStyle
Indicates that one entity is designed, built, or decorated in the architectural style characteristic of a particular railway station.
- 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_69f349cd7c148190aa99192b126d1527 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f73397e5488190a9cdce98a0ac0383 |
completed | May 3, 2026, 11:38 a.m. |
| PD | Predicate disambiguation | batch_69f732f3ad248190b4bcbf589d000f0d |
completed | May 3, 2026, 11:35 a.m. |
Created at: May 1, 2026, 2:02 a.m.