Triple

T876799
Position Surface form Disambiguated ID Type / Status
Subject Lisbon Metro E18934 entity
Predicate hasDepot P2413 FINISHED
Object Carnide depot
Carnide depot is a maintenance and storage facility serving the Lisbon Metro system in Lisbon, Portugal.
E105487 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: Carnide depot | Statement: [Lisbon Metro, hasDepot, Carnide depot]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Carnide depot
Context triple: [Lisbon Metro, hasDepot, Carnide depot]
  • A. Fürth depot
    Fürth depot is a maintenance and storage facility serving the Nuremberg U-Bahn rapid transit system in the Fürth area of Germany.
  • B. Pontinha depot
    Pontinha depot is a maintenance and storage facility serving the Lisbon Metro system in Lisbon, Portugal.
  • C. Langwasser depot
    Langwasser depot is a maintenance and storage facility serving the Nuremberg U-Bahn rapid transit system in Nuremberg, Germany.
  • D. Ticomán depot
    Ticomán depot is a maintenance and storage facility serving trains of the Mexico City Metro system.
  • E. Wellington Yard
    Wellington Yard is a railway yard associated with Wellington station, used for storing, organizing, and managing trains and rolling stock.
  • 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: Carnide depot
Triple: [Lisbon Metro, hasDepot, Carnide depot]
Generated description
Carnide depot is a maintenance and storage facility serving the Lisbon Metro system in Lisbon, Portugal.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Carnide depot
Target entity description: Carnide depot is a maintenance and storage facility serving the Lisbon Metro system in Lisbon, Portugal.
  • A. Fürth depot
    Fürth depot is a maintenance and storage facility serving the Nuremberg U-Bahn rapid transit system in the Fürth area of Germany.
  • B. Pontinha depot
    Pontinha depot is a maintenance and storage facility serving the Lisbon Metro system in Lisbon, Portugal.
  • C. Langwasser depot
    Langwasser depot is a maintenance and storage facility serving the Nuremberg U-Bahn rapid transit system in Nuremberg, Germany.
  • D. Ticomán depot
    Ticomán depot is a maintenance and storage facility serving trains of the Mexico City Metro system.
  • E. Wellington Yard
    Wellington Yard is a railway yard associated with Wellington station, used for storing, organizing, and managing trains and rolling stock.
  • 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_69a4938db1f081909bcd1ad2713b6096 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4acaf30a48190a10ed7fee464c444 completed March 1, 2026, 9:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7c01d1a7081909da3d8b2aadaed6f completed March 4, 2026, 5:16 a.m.
NEDg Description generation batch_69a7c09d63a08190b30d77db969ad5f0 completed March 4, 2026, 5:18 a.m.
NED2 Entity disambiguation (via description) batch_69a7c0fef22c8190b49fbaef969ba098 completed March 4, 2026, 5:19 a.m.
Created at: March 1, 2026, 7:39 p.m.