Triple
T2361481
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Butovskaya Line |
E47280
|
entity |
| Predicate | hasDepot |
P2413
|
FINISHED |
| Object |
Butovo depot
Butovo depot is the maintenance and storage facility serving the Butovskaya Line of the Moscow Metro in Moscow, Russia.
|
E259721
|
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: Butovo depot | Statement: [Butovskaya Line, hasDepot, Butovo depot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Butovo depot Context triple: [Butovskaya Line, hasDepot, Butovo depot]
-
A.
Vykhino depot
Vykhino depot is a maintenance and storage facility serving trains of the Tagansko–Krasnopresnenskaya Line of the Moscow Metro.
-
B.
Zavod Barrikady station
Zavod Barrikady station is a stop on the Volgograd Metrotram system in Volgograd, Russia, serving the industrial area around the historic Barrikady factory.
-
C.
Fürth depot
Fürth depot is a maintenance and storage facility serving the Nuremberg U-Bahn rapid transit system in the Fürth area of Germany.
-
D.
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.
-
E.
Carnide depot
Carnide depot is a maintenance and storage facility serving the Lisbon Metro system in Lisbon, Portugal.
- 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: Butovo depot Triple: [Butovskaya Line, hasDepot, Butovo depot]
Generated description
Butovo depot is the maintenance and storage facility serving the Butovskaya Line of the Moscow Metro in Moscow, Russia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Butovo depot Target entity description: Butovo depot is the maintenance and storage facility serving the Butovskaya Line of the Moscow Metro in Moscow, Russia.
-
A.
Vykhino depot
Vykhino depot is a maintenance and storage facility serving trains of the Tagansko–Krasnopresnenskaya Line of the Moscow Metro.
-
B.
Zavod Barrikady station
Zavod Barrikady station is a stop on the Volgograd Metrotram system in Volgograd, Russia, serving the industrial area around the historic Barrikady factory.
-
C.
Fürth depot
Fürth depot is a maintenance and storage facility serving the Nuremberg U-Bahn rapid transit system in the Fürth area of Germany.
-
D.
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.
-
E.
Carnide depot
Carnide depot is a maintenance and storage facility serving the Lisbon Metro system in Lisbon, Portugal.
- 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_69a88a1a4a6081908645b0f2914521ab |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abc723c66481908a9b94991f651b3b |
completed | March 7, 2026, 6:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aea88cf3308190bdb5aac38aded823 |
completed | March 9, 2026, 11:01 a.m. |
| NEDg | Description generation | batch_69aea91ce164819091aa24b287f9fb8e |
completed | March 9, 2026, 11:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69aea999b864819084134c670e7c5d9c |
completed | March 9, 2026, 11:06 a.m. |
Created at: March 4, 2026, 7:55 p.m.