Triple

T6938218
Position Surface form Disambiguated ID Type / Status
Subject Bucharest Metro E160604 entity
Predicate hasDepot P2413 FINISHED
Object Berceni depot
Berceni depot is a maintenance and storage facility serving the Bucharest Metro system in Bucharest, Romania.
 E629470 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: Berceni depot | Statement: [Bucharest Metro, hasDepot, Berceni depot]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Berceni depot
Context triple: [Bucharest Metro, hasDepot, Berceni depot]
  • A. Carnide depot
    Carnide depot is a maintenance and storage facility serving the Lisbon Metro system in Lisbon, Portugal.
  • B. Vennes depot
    Vennes depot is a maintenance and storage facility serving Lausanne’s metro system in Switzerland.
  • 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. Bachet depot
    Bachet depot is a major tram maintenance and storage facility serving the public transport system in Geneva, Switzerland.
  • E. Gogar depot
    Gogar depot is the main maintenance and operations facility for the Edinburgh Trams light rail system in Edinburgh, Scotland.
  • 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: Berceni depot
Triple: [Bucharest Metro, hasDepot, Berceni depot]
Generated description
Berceni depot is a maintenance and storage facility serving the Bucharest Metro system in Bucharest, Romania.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Berceni depot
Target entity description: Berceni depot is a maintenance and storage facility serving the Bucharest Metro system in Bucharest, Romania.
  • A. Carnide depot
    Carnide depot is a maintenance and storage facility serving the Lisbon Metro system in Lisbon, Portugal.
  • B. Vennes depot
    Vennes depot is a maintenance and storage facility serving Lausanne’s metro system in Switzerland.
  • 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. Bachet depot
    Bachet depot is a major tram maintenance and storage facility serving the public transport system in Geneva, Switzerland.
  • E. Gogar depot
    Gogar depot is the main maintenance and operations facility for the Edinburgh Trams light rail system in Edinburgh, Scotland.
  • 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_69c6884f3db4819080ad65da69386206 completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6da62d2f88190968d3fea538a95c9 completed March 27, 2026, 7:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7515509148190b5739cdf8cd7a28a completed March 28, 2026, 3:56 a.m.
NEDg Description generation batch_69c752c9b3d08190960d3c1aa88a93a7 completed March 28, 2026, 4:02 a.m.
NED2 Entity disambiguation (via description) batch_69c7537ea24c819081bb672d43d4a373 completed March 28, 2026, 4:05 a.m.
Created at: March 27, 2026, 2:28 p.m.