Triple
T676283
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tyne and Wear Metro |
E13083
|
entity |
| Predicate | introducedRollingStock |
P17949
|
FINISHED |
| Object | 1980 |
—
|
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: 1980 | Statement: [Tyne and Wear Metro, introducedRollingStock, 1980]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: introducedRollingStock Context triple: [Tyne and Wear Metro, introducedRollingStock, 1980]
-
A.
usesRollingStock
Indicates that one entity employs or operates specific rolling stock (such as rail vehicles) in its activities or services.
-
B.
rollingStockType
Indicates the specific category or type of railway rolling stock associated with an entity (e.g., locomotive, passenger car, freight wagon).
-
C.
replacedRollingStock
Indicates that one rolling stock asset has been substituted or superseded by another in service or operational use.
-
D.
rollingStockOperator
Indicates that an entity operates or manages rolling stock, such as trains or rail vehicles, in a railway system.
-
E.
railroadClass
Indicates the classification or category of a railroad according to an established system (e.g., by size, revenue, or regulatory status).
- 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_69a4933d3bf88190972041cd8cf143b9 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a04b2ae881908a5c23453bef8572 |
completed | March 1, 2026, 8:23 p.m. |
| PD | Predicate disambiguation | batch_69a49d1bbd0c81909cfbec30bd17bde7 |
completed | March 1, 2026, 8:10 p.m. |
| PDg | Predicate description generation | batch_69a49ebf33c481909949526cb8f223dd |
completed | March 1, 2026, 8:17 p.m. |
Created at: March 1, 2026, 7:36 p.m.