Triple
T8771103
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 71-619 |
E208462
|
entity |
| Predicate | hasBogieCount |
P85304
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [71-619, hasBogieCount, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBogieCount Context triple: [71-619, hasBogieCount, 2]
-
A.
bogieType
Indicates the specific configuration or classification of a vehicle’s bogie (wheel assembly) used in its design or operation.
-
B.
hasNumberOfBridges
Indicates the quantitative relationship specifying how many bridges are associated with a given entity.
-
C.
hasRollingStockOnDisplay
Indicates that a location or entity has railway rolling stock (such as locomotives or carriages) exhibited for public viewing.
-
D.
numberOfBores
Indicates the relationship specifying how many bores (e.g., cylindrical holes or channels) are present in or associated with an object.
-
E.
hasChicane
Indicates that one entity incorporates or features a chicane (a sharp, S-shaped bend or series of bends), typically in the context of a track, route, or path.
- 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_69ca835edb4481909b4aafb616dc5eb7 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5f2b08f881909f3d4fab2eda1d67 |
completed | March 31, 2026, 11:56 p.m. |
| PD | Predicate disambiguation | batch_69cc5c1aff3881908be6a9cbc9f50461 |
completed | March 31, 2026, 11:43 p.m. |
| PDg | Predicate description generation | batch_69cc5cfddef48190aee764ee7b25bae9 |
completed | March 31, 2026, 11:47 p.m. |
Created at: March 30, 2026, 6:41 p.m.