Triple
T9542964
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vykhino depot |
E230201
|
entity |
| Predicate | railwayRollingStockMaintained |
P5426
|
FINISHED |
| Object | Moscow Metro trains |
—
|
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: Moscow Metro trains | Statement: [Vykhino depot, railwayRollingStockMaintained, Moscow Metro trains]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: railwayRollingStockMaintained Context triple: [Vykhino depot, railwayRollingStockMaintained, Moscow Metro trains]
-
A.
ownedRollingStock
Indicates that one entity possesses or has ownership rights over specific rolling stock (such as trains, railcars, or locomotives).
-
B.
formerRollingStock
Indicates that an entity was previously used as rolling stock (e.g., railway vehicles) but no longer serves in that capacity.
-
C.
railcode
Indicates that an entity is associated with a specific railway code used for identification or classification within a rail system.
-
D.
usesRollingStock
chosen
Indicates that one entity employs or operates specific rolling stock (such as rail vehicles) in its activities or services.
-
E.
line1RollingStock
Indicates that the rolling stock (e.g., train cars or locomotives) is assigned to or operating on the first line in a multi-line rail system.
- 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_69ca847c70b8819088a0a0bad64a50d6 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd98e9be048190bf1f01884ff7c362 |
completed | April 1, 2026, 10:15 p.m. |
| PD | Predicate disambiguation | batch_69ccd58bd21881908b860e3ee469af13 |
completed | April 1, 2026, 8:21 a.m. |
Created at: March 30, 2026, 8:01 p.m.