Triple

T18957970
Position Surface form Disambiguated ID Type / Status
Subject Madrid–Alicante railway E463828 entity
Predicate servesCity P82 FINISHED
Object Villena 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: Villena | Statement: [Madrid–Alicante railway, servesCity, Villena]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Villena
Context triple: [Madrid–Alicante railway, servesCity, Villena]
  • A. Villena chosen
    Villena is a historic city in the province of Alicante, Spain, known for its medieval castle, archaeological heritage, and traditional festivals.
  • B. Alhué
    Alhué is a rural commune and town in central Chile known for its agricultural activities and traditional countryside character within the Santiago Metropolitan Region.
  • C. Valencia de las Torres
    Valencia de las Torres is a small rural municipality in the Las Vegas Altas del Guadiana comarca of the Extremadura region in western Spain.
  • D. Burriana
    Burriana is a coastal town in Spain’s Valencian Community known for its Mediterranean beaches and role as a holiday resort on the Costa del Azahar.
  • E. Villar del Cobo
    Villar del Cobo is a small rural municipality in the province of Teruel, Aragon, Spain, situated in the mountainous Sierra de Albarracín comarca.
  • 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_69d8dcffc278819086792a4ebfddfafa completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5d5cee8348190b6506b10aed6c58a completed April 20, 2026, 7:29 a.m.
Created at: April 10, 2026, noon