Triple
T9542944
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Planernoye depot |
E230200
|
entity |
| Predicate | hasNameInLanguage |
P15
|
FINISHED |
| Object | Планерное депо |
E230200
|
NE 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: Планерное депо | Statement: [Planernoye depot, hasNameInLanguage, Планерное депо]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Планерное депо Context triple: [Planernoye depot, hasNameInLanguage, Планерное депо]
-
A.
Planernoye depot
chosen
Planernoye depot is a maintenance and storage facility serving trains on Moscow’s Tagansko–Krasnopresnenskaya metro line.
-
B.
Moskovskoe depot
Moskovskoe depot is a maintenance and storage facility serving the Minsk Metro system in Minsk, Belarus.
-
C.
Vykhino depot
Vykhino depot is a maintenance and storage facility serving trains of the Tagansko–Krasnopresnenskaya Line of the Moscow Metro.
-
D.
Zelenoluzhskoe depot
Zelenoluzhskoe depot is a maintenance and storage facility serving trains of the Minsk Metro system in Minsk, Belarus.
-
E.
Planernaya
Planernaya is a Moscow Metro station that serves as the northwestern terminus of the Tagansko–Krasnopresnenskaya Line.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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. |
| NED1 | Entity disambiguation (via context triple) | batch_69d14c6538b08190a9f81304214a876d |
completed | April 4, 2026, 5:37 p.m. |
Created at: March 30, 2026, 8:01 p.m.