Triple
T31149864
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zelenoluzhskoe depot |
E794041
|
entity |
| Predicate | servedRollingStockType |
P1305
|
FINISHED |
| Object | electric multiple units |
—
|
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: electric multiple units | Statement: [Zelenoluzhskoe depot, servedRollingStockType, electric multiple units]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servedRollingStockType Context triple: [Zelenoluzhskoe depot, servedRollingStockType, electric multiple units]
-
A.
servicesRollingStock
Indicates that an entity provides operational services to or performs work on rolling stock (such as trains or rail vehicles).
-
B.
passengerRollingStock
Indicates that the rolling stock is designed or used for carrying passengers rather than freight or other purposes.
-
C.
rollingStockType
chosen
Indicates the specific category or type of railway rolling stock associated with an entity (e.g., locomotive, passenger car, freight wagon).
-
D.
operatedRollingStockClass
Indicates that an entity (such as a company or operator) has operated a specific class or type of rolling stock (e.g., trains or rail vehicles).
-
E.
ownedRollingStock
Indicates that one entity possesses or has ownership rights over specific rolling stock (such as trains, railcars, or locomotives).
- F. None of above.
Provenance (3 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_69f224d41bb48190a5621cd1485e3a30 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69feb5e66224819083b87c3707a5a5e0 |
completed | May 9, 2026, 4:19 a.m. |
| PD | Predicate disambiguation | batch_69feb3bd700c8190991ed200cd3c04db |
completed | May 9, 2026, 4:10 a.m. |
Created at: April 29, 2026, 9:06 p.m.