Triple
T10857121
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | British Rail Class 37 |
E256295
|
entity |
| Predicate | locomotiveWeight |
P96098
|
FINISHED |
| Object | approximately 100 tonnes |
—
|
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: approximately 100 tonnes | Statement: [British Rail Class 37, locomotiveWeight, approximately 100 tonnes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locomotiveWeight Context triple: [British Rail Class 37, locomotiveWeight, approximately 100 tonnes]
-
A.
typicalLocomotiveClass
Indicates that one locomotive class is the standard or most commonly used class for a given context, operator, or service.
-
B.
locomotiveNumber
Indicates the identifying number assigned to a locomotive in the relationship.
-
C.
hasLocomotive
Indicates that one entity possesses or is equipped with a locomotive as part of its composition or operation.
-
D.
locomotiveConfiguration
Indicates the specific arrangement and type of power and running units (e.g., wheel or axle layout) that define how a locomotive is configured.
-
E.
laterLocomotiveType
Indicates that one locomotive type succeeds or comes after another in time, representing a later development or version in locomotive design.
- 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_69d6aa83d1448190a66d93c32394d21f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7513695ac8190b5812e977a422c37 |
completed | April 9, 2026, 7:11 a.m. |
| PD | Predicate disambiguation | batch_69d70d308dfc81908792f98cfb871392 |
completed | April 9, 2026, 2:21 a.m. |
| PDg | Predicate description generation | batch_69d7101c96708190808fef73199e8482 |
completed | April 9, 2026, 2:34 a.m. |
Created at: April 8, 2026, 9:20 p.m.