Triple
T9313935
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Minsk Metro |
E224070
|
entity |
| Predicate | hasDepot |
P2413
|
FINISHED |
| Object |
Zelenoluzhskoe depot
Zelenoluzhskoe depot is a maintenance and storage facility serving trains of the Minsk Metro system in Minsk, Belarus.
|
E794041
|
NE FINISHED |
How this triple was built (4 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: Zelenoluzhskoe depot | Statement: [Minsk Metro, hasDepot, Zelenoluzhskoe depot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zelenoluzhskoe depot Context triple: [Minsk Metro, hasDepot, Zelenoluzhskoe depot]
-
A.
Moskovskoe depot
Moskovskoe depot is a maintenance and storage facility serving the Minsk Metro system in Minsk, Belarus.
-
B.
Mogilevskoe depot
Mogilevskoe depot is a maintenance and storage facility serving the rolling stock of the Minsk Metro system in Belarus.
-
C.
Vykhino depot
Vykhino depot is a maintenance and storage facility serving trains of the Tagansko–Krasnopresnenskaya Line of the Moscow Metro.
-
D.
Kholodna Hora depot
Kholodna Hora depot is a maintenance and storage facility serving the Kharkiv Metro system in Kharkiv, Ukraine.
-
E.
Zavod Krasny Oktyabr station
Zavod Krasny Oktyabr station is a tram-station stop on the Volgograd Metrotram system serving the industrial area around the historic Krasny Oktyabr (Red October) factory in Volgograd, Russia.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Zelenoluzhskoe depot Triple: [Minsk Metro, hasDepot, Zelenoluzhskoe depot]
Generated description
Zelenoluzhskoe depot is a maintenance and storage facility serving trains of the Minsk Metro system in Minsk, Belarus.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Zelenoluzhskoe depot Target entity description: Zelenoluzhskoe depot is a maintenance and storage facility serving trains of the Minsk Metro system in Minsk, Belarus.
-
A.
Moskovskoe depot
Moskovskoe depot is a maintenance and storage facility serving the Minsk Metro system in Minsk, Belarus.
-
B.
Mogilevskoe depot
Mogilevskoe depot is a maintenance and storage facility serving the rolling stock of the Minsk Metro system in Belarus.
-
C.
Vykhino depot
Vykhino depot is a maintenance and storage facility serving trains of the Tagansko–Krasnopresnenskaya Line of the Moscow Metro.
-
D.
Kholodna Hora depot
Kholodna Hora depot is a maintenance and storage facility serving the Kharkiv Metro system in Kharkiv, Ukraine.
-
E.
Zavod Krasny Oktyabr station
Zavod Krasny Oktyabr station is a tram-station stop on the Volgograd Metrotram system serving the industrial area around the historic Krasny Oktyabr (Red October) factory in Volgograd, Russia.
- F. None of above. chosen
Provenance (5 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_69ca8425f4fc81909c1c586e9a5b7530 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd20b048a081909fd7ec0b6b863063 |
completed | April 1, 2026, 1:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0f3a3fb288190ac38f8df19eb1e79 |
completed | April 4, 2026, 11:19 a.m. |
| NEDg | Description generation | batch_69d0f55656288190a989deec892cb2f9 |
completed | April 4, 2026, 11:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d0f5bf64548190b40e97b279db5105 |
completed | April 4, 2026, 11:27 a.m. |
Created at: March 30, 2026, 7:37 p.m.