Triple

T10644775
Position Surface form Disambiguated ID Type / Status
Subject Maresme E250808 entity
Predicate hasMunicipality P847 FINISHED
Object Cabrera de Mar
Cabrera de Mar is a coastal municipality in the Maresme comarca of Catalonia, Spain, known for its Mediterranean beaches and archaeological heritage.
 E878044 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: Cabrera de Mar | Statement: [Maresme, hasMunicipality, Cabrera de Mar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Cabrera de Mar
Context triple: [Maresme, hasMunicipality, Cabrera de Mar]
  • A. Cervera
    Cervera is a Spanish surname historically associated with notable figures such as Admiral Pascual Cervera y Topete.
  • B. La Serna
    La Serna is a station on Madrid Metro’s Line C-5 commuter rail corridor serving the Fuenlabrada area in the Community of Madrid, Spain.
  • C. Castro Marim
    Castro Marim is a town and municipality in Portugal’s Algarve region, near the Spanish border, known for its historic castle and salt marshes.
  • D. Rebollo
    Rebollo is a Spanish surname most notably associated with Antonio Rebollo, the Paralympic archer who lit the Olympic cauldron at the 1992 Barcelona Games.
  • E. Argüelles
    Argüelles is a Madrid Metro station serving the Argüelles neighborhood, providing an interchange between several central metro lines.
  • 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: Cabrera de Mar
Triple: [Maresme, hasMunicipality, Cabrera de Mar]
Generated description
Cabrera de Mar is a coastal municipality in the Maresme comarca of Catalonia, Spain, known for its Mediterranean beaches and archaeological heritage.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Cabrera de Mar
Target entity description: Cabrera de Mar is a coastal municipality in the Maresme comarca of Catalonia, Spain, known for its Mediterranean beaches and archaeological heritage.
  • A. Cervera
    Cervera is a Spanish surname historically associated with notable figures such as Admiral Pascual Cervera y Topete.
  • B. La Serna
    La Serna is a station on Madrid Metro’s Line C-5 commuter rail corridor serving the Fuenlabrada area in the Community of Madrid, Spain.
  • C. Castro Marim
    Castro Marim is a town and municipality in Portugal’s Algarve region, near the Spanish border, known for its historic castle and salt marshes.
  • D. Rebollo
    Rebollo is a Spanish surname most notably associated with Antonio Rebollo, the Paralympic archer who lit the Olympic cauldron at the 1992 Barcelona Games.
  • E. Argüelles
    Argüelles is a Madrid Metro station serving the Argüelles neighborhood, providing an interchange between several central metro lines.
  • 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_69d6aa5a4c4881908f39be6efe5981e5 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6dfd04ca88190ac4fffd13c1f33a8 completed April 8, 2026, 11:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69d97a4dd4e48190ba7d0291686702e6 completed April 10, 2026, 10:31 p.m.
NEDg Description generation batch_69d97cc07100819088683a0d79b2baa0 completed April 10, 2026, 10:42 p.m.
NED2 Entity disambiguation (via description) batch_69d97e0cda0c8190af5013b971b2ad3c completed April 10, 2026, 10:47 p.m.
Created at: April 8, 2026, 9:05 p.m.