Triple

T11819612
Position Surface form Disambiguated ID Type / Status
Subject Kingdom of Córdoba E281088 entity
Predicate hasCity P316 FINISHED
Object Lucena E327410 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: Lucena | Statement: [Kingdom of Córdoba, hasCity, Lucena]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lucena
Context triple: [Kingdom of Córdoba, hasCity, Lucena]
  • A. Lucena
    Lucena is a coastal municipality in the state of Paraíba, Brazil, known for its beaches and proximity to the capital city João Pessoa.
  • B. Lucena chosen
    Lucena is a historic city in the province of Córdoba, Andalusia, southern Spain, known for its rich cultural heritage and former Jewish community.
  • C. Lucena
    Lucena is a coastal city in the Philippines that serves as the capital and commercial hub of Quezon Province in the Southern Tagalog region.
  • D. Aranjuez
    Aranjuez is a historic town in central Spain renowned for its royal palace, extensive gardens, and cultural heritage, and is located within the Community of Madrid.
  • E. Badajoz
    Badajoz is a historic city in western Spain near the Portuguese border, known for its medieval fortress and role as a strategic frontier stronghold.
  • 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_69d6ab26aae88190b2489efcb2a24234 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8a5e87e488190905bc3bb6d721e56 completed April 10, 2026, 7:25 a.m.
NED1 Entity disambiguation (via context triple) batch_69f131e1891081909cfbd9e2eb168808 completed April 28, 2026, 10:17 p.m.
Created at: April 8, 2026, 9:42 p.m.