Triple

T17471153
Position Surface form Disambiguated ID Type / Status
Subject Denia E425416 entity
Predicate hasOfficialName P66 FINISHED
Object Dénia NE NERFINISHED

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: Dénia | Statement: [Denia, hasOfficialName, Dénia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dénia
Context triple: [Denia, hasOfficialName, Dénia]
  • A. Denia chosen
    Denia is a coastal city on Spain’s Costa Blanca known for its historic castle, Mediterranean beaches, and vibrant port.
  • B. Alcoy
    Alcoy is an industrial and historically significant city in southeastern Spain, known for its textile heritage, modernist architecture, and famous Moors and Christians festival.
  • C. Altea
    Altea is a picturesque coastal town on Spain’s Costa Blanca, known for its whitewashed old quarter, blue-domed church, and vibrant arts scene.
  • D. Alicante
    Alicante is a historic Mediterranean port city in southeastern Spain known for its beaches, castle-topped hill, and role as a major tourist and commercial center.
  • E. Mazarrón
    Mazarrón is a coastal town and popular tourist destination in Spain’s Region of Murcia, known for its beaches, marina, and archaeological heritage.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d889dbc2e88190b18ea6115e819258 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e451abab908190b6d9d8a64f7c2ea3 completed April 19, 2026, 3:53 a.m.
Created at: April 10, 2026, 5:47 a.m.