Triple

T13721834
Position Surface form Disambiguated ID Type / Status
Subject Southern Spain E329055 entity
Predicate hasCity P316 FINISHED
Object Benalmádena E659584 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: Benalmádena | Statement: [Southern Spain, hasCity, Benalmádena]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Benalmádena
Context triple: [Southern Spain, hasCity, Benalmádena]
  • A. Benalmádena chosen
    Benalmádena is a coastal resort town on Spain’s Costa del Sol, known for its beaches, marina, and tourist attractions near Málaga.
  • B. Estepona
    Estepona is a coastal resort town on Spain’s Costa del Sol, known for its Mediterranean beaches, marina, and whitewashed old town.
  • C. Marbella
    Marbella is a popular resort city on Spain’s Costa del Sol, known for its Mediterranean beaches, luxury marinas, upscale nightlife, and historic old town.
  • D. Torremolinos
    Torremolinos is a popular seaside resort town on Spain’s Costa del Sol, known for its beaches, nightlife, and tourism-focused economy.
  • E. Fuengirola
    Fuengirola is a popular coastal resort town on Spain’s Costa del Sol, known for its long sandy beaches, tourism infrastructure, and proximity to Málaga.
  • 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_69d80770b9bc81909f70c8c317d53cff completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69de01f3b46481909ceedfa78e9ca92b completed April 14, 2026, 8:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7c0dd20348190adfb1712de06088c completed May 3, 2026, 9:40 p.m.
Created at: April 9, 2026, 9:55 p.m.