Triple
T8456904
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | British Rail Class 373 |
E199940
|
entity |
| Predicate | trainsetType |
P82750
|
FINISHED |
| Object | fixed-formation multiple unit |
—
|
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: fixed-formation multiple unit | Statement: [British Rail Class 373, trainsetType, fixed-formation multiple unit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainsetType Context triple: [British Rail Class 373, trainsetType, fixed-formation multiple unit]
-
A.
trainTypeUsed
Indicates that a specific type or category of train is employed or operated in a given context or service.
-
B.
trainShedType
Indicates the specific kind or classification of shed associated with a train or railway facility.
-
C.
trainingDataType
Indicates the type or category of data used for training a model, system, or process.
-
D.
coachType
Indicates the specific category or role of a coach associated with an entity (e.g., head coach, assistant coach, position coach).
-
E.
railSystemType
Indicates the specific category or classification of a rail transportation system that an entity belongs to or operates within.
- 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_69ca8318231881908fd1bc1c4d45d286 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe48f180c8190a71cf9d7248ade60 |
completed | March 31, 2026, 3:13 p.m. |
| PD | Predicate disambiguation | batch_69cbd0fc634481909842c0a30077bfde |
completed | March 31, 2026, 1:49 p.m. |
| PDg | Predicate description generation | batch_69cbe12dd0b88190a38ec4d15dcc870b |
completed | March 31, 2026, 2:58 p.m. |
Created at: March 30, 2026, 6:10 p.m.