Triple
T4561530
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | USRA standard steam locomotive designs |
E121801
|
entity |
| Predicate | locomotiveWheelArrangementStandard |
P57679
|
FINISHED |
| Object | 4-6-2 |
—
|
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: 4-6-2 | Statement: [USRA standard steam locomotive designs, locomotiveWheelArrangementStandard, 4-6-2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locomotiveWheelArrangementStandard Context triple: [USRA standard steam locomotive designs, locomotiveWheelArrangementStandard, 4-6-2]
-
A.
railcode
Indicates that an entity is associated with a specific railway code used for identification or classification within a rail system.
-
B.
locomotiveNumber
Indicates the identifying number assigned to a locomotive in the relationship.
-
C.
rollingStockType
Indicates the specific category or type of railway rolling stock associated with an entity (e.g., locomotive, passenger car, freight wagon).
-
D.
locomotiveWorks
Indicates a relationship where an entity is a facility or company that builds, repairs, or maintains locomotives.
-
E.
bogieType
Indicates the specific configuration or classification of a vehicle’s bogie (wheel assembly) used in its design or operation.
- 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_69bd463f156881908a99aca69c5721ac |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd582d98fc8190a760dbb5f20c775d |
completed | March 20, 2026, 2:22 p.m. |
| PD | Predicate disambiguation | batch_69bd52254c648190a5144cfe8fa7e409 |
completed | March 20, 2026, 1:56 p.m. |
| PDg | Predicate description generation | batch_69bd56f6e75481909c487a94a2c2d0ba |
completed | March 20, 2026, 2:17 p.m. |
Created at: March 20, 2026, 1:09 p.m.