Triple
T8211556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ernest S. Marsh |
E191827
|
entity |
| Predicate | hasTrainSetType |
P56947
|
FINISHED |
| Object | passenger coaches |
—
|
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: passenger coaches | Statement: [Ernest S. Marsh, hasTrainSetType, passenger coaches]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTrainSetType Context triple: [Ernest S. Marsh, hasTrainSetType, passenger coaches]
-
A.
hasTrainingType
Indicates that an entity is associated with or characterized by a specific type or category of training.
-
B.
hasTrained
Indicates that one entity has provided training or instruction to another entity.
-
C.
hasTrainingFor
Indicates that an entity has received or possesses training that prepares it for performing a specific task, role, or function.
-
D.
hasTrainingFunction
Indicates that one entity serves as a training function or mechanism for another entity.
-
E.
trainTypeUsed
chosen
Indicates that a specific type or category of train is employed or operated in a given context 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_69ca82c8c054819087fedd9a5436b8a3 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb76dec42c819090252fe186a68d34 |
completed | March 31, 2026, 7:25 a.m. |
| PD | Predicate disambiguation | batch_69cb36ad01ac81909609b15f6a6c8581 |
completed | March 31, 2026, 2:51 a.m. |
Created at: March 30, 2026, 5:44 p.m.