Triple
T2572390
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UTDC ICTS Mark I vehicles |
E57693
|
entity |
| Predicate | trainControl |
P39873
|
FINISHED |
| Object | automatic train control |
—
|
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: automatic train control | Statement: [UTDC ICTS Mark I vehicles, trainControl, automatic train control]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainControl Context triple: [UTDC ICTS Mark I vehicles, trainControl, automatic train control]
-
A.
trainingModel
Indicates that an entity is engaged in the process of teaching, adjusting, or optimizing a model using data or experience.
-
B.
trainingUse
Indicates that something is used for training purposes, such as preparing, educating, or improving the skills or performance of an entity.
-
C.
trainingParadigm
Indicates the specific methodological framework or approach used to train an entity (such as a model, system, or agent).
-
D.
trainConfiguration
Indicates the specific arrangement and composition of train elements (such as locomotives and cars) used together for a particular operation or service.
-
E.
trainingMethod
Indicates the specific approach, technique, or procedure used to train an entity (such as a person, model, or system).
- 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_69ab4a51410081908501dcf8bad9adc4 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd3853c848190970e8a2da16d726d |
completed | March 7, 2026, 7:28 a.m. |
| PD | Predicate disambiguation | batch_69abd0ce4dcc8190b17a65abf9bd1bb0 |
completed | March 7, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69abd251b48c8190862c7b39ea1bf8ea |
completed | March 7, 2026, 7:22 a.m. |
Created at: March 6, 2026, 9:48 p.m.