Triple

T10645068
Position Surface form Disambiguated ID Type / Status
Subject Metropolitan Area of Barcelona E250814 entity
Predicate containsMunicipality P852 FINISHED
Object Castelldefels E266955 NE FINISHED

How this triple was built (2 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: Castelldefels | Statement: [Metropolitan Area of Barcelona, containsMunicipality, Castelldefels]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Castelldefels
Context triple: [Metropolitan Area of Barcelona, containsMunicipality, Castelldefels]
  • A. Castelldefels chosen
    Castelldefels is a coastal town near Barcelona in Catalonia, Spain, known for its long sandy beaches and residential character.
  • B. Calella
    Calella is a coastal town and popular tourist destination on the Mediterranean in the Maresme comarca of Catalonia, Spain.
  • C. Banyoles
    Banyoles is a town in Catalonia, Spain, best known for its large natural lake and scenic surroundings.
  • D. Vilassar de Mar
    Vilassar de Mar is a coastal town and municipality on the Mediterranean in the Maresme comarca of Catalonia, Spain, known for its beaches and residential character.
  • E. Castellolí
    Castellolí is a small municipality in the Anoia comarca of Catalonia, Spain, known for its rural setting and proximity to the Montserrat mountain range.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d6aa5a4c4881908f39be6efe5981e5 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6dfe120908190ab91c38d57133739 completed April 8, 2026, 11:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69f489b62b808190a3fc89e73e8ae2f7 completed May 1, 2026, 11:08 a.m.
Created at: April 8, 2026, 9:05 p.m.