Triple

T2045764
Position Surface form Disambiguated ID Type / Status
Subject Tagansko–Krasnopresnenskaya Line E45446 entity
Predicate hasDepot P2413 FINISHED
Object Planernoye depot
Planernoye depot is a maintenance and storage facility serving trains on Moscow’s Tagansko–Krasnopresnenskaya metro line.
 E230200 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: Planernoye depot | Statement: [Tagansko–Krasnopresnenskaya Line, hasDepot, Planernoye depot]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Planernoye depot
Context triple: [Tagansko–Krasnopresnenskaya Line, hasDepot, Planernoye depot]
  • A. Carnide depot
    Carnide depot is a maintenance and storage facility serving the Lisbon Metro system in Lisbon, Portugal.
  • B. Nopo Depot
    Nopo Depot is a maintenance and storage facility serving the Busan Metro system in Busan, South Korea.
  • C. Bachet depot
    Bachet depot is a major tram maintenance and storage facility serving the public transport system in Geneva, Switzerland.
  • D. Fürth depot
    Fürth depot is a maintenance and storage facility serving the Nuremberg U-Bahn rapid transit system in the Fürth area of Germany.
  • E. Prospekt Vernadskogo station
    Prospekt Vernadskogo station is a Moscow Metro station named after the nearby Prospekt Vernadskogo avenue, serving passengers in the city’s southwestern district.
  • 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: Planernoye depot
Triple: [Tagansko–Krasnopresnenskaya Line, hasDepot, Planernoye depot]
Generated description
Planernoye depot is a maintenance and storage facility serving trains on Moscow’s Tagansko–Krasnopresnenskaya metro line.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Planernoye depot
Target entity description: Planernoye depot is a maintenance and storage facility serving trains on Moscow’s Tagansko–Krasnopresnenskaya metro line.
  • A. Carnide depot
    Carnide depot is a maintenance and storage facility serving the Lisbon Metro system in Lisbon, Portugal.
  • B. Nopo Depot
    Nopo Depot is a maintenance and storage facility serving the Busan Metro system in Busan, South Korea.
  • C. Bachet depot
    Bachet depot is a major tram maintenance and storage facility serving the public transport system in Geneva, Switzerland.
  • D. Fürth depot
    Fürth depot is a maintenance and storage facility serving the Nuremberg U-Bahn rapid transit system in the Fürth area of Germany.
  • E. Prospekt Vernadskogo station
    Prospekt Vernadskogo station is a Moscow Metro station named after the nearby Prospekt Vernadskogo avenue, serving passengers in the city’s southwestern district.
  • 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_69a8891948208190ab7898da21824c77 completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abb9728f688190939d7c4df524f9b4 completed March 7, 2026, 5:36 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae200125f081909ab40b6a04adaa25 completed March 9, 2026, 1:18 a.m.
NEDg Description generation batch_69ae242933288190ad1f2c9f4ce1e968 completed March 9, 2026, 1:36 a.m.
NED2 Entity disambiguation (via description) batch_69ae248a2b8481908fa4b0c000971d11 completed March 9, 2026, 1:38 a.m.
Created at: March 4, 2026, 7:39 p.m.