Triple

T17704844
Position Surface form Disambiguated ID Type / Status
Subject Yerbas Buenas E441402 entity
Predicate borderingAdministrativeUnit P224 FINISHED
Object Linares 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: Linares | Statement: [Yerbas Buenas, borderingAdministrativeUnit, Linares]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Linares
Context triple: [Yerbas Buenas, borderingAdministrativeUnit, Linares]
  • A. Linares chosen
    Linares is a provincial capital and agricultural city in Chile’s Maule Region, known for its surrounding farmlands and wine production.
  • B. Linares
    Linares is a city in the province of Jaén in Andalusia, southern Spain, historically known for its mining industry and cultural heritage.
  • C. Linares
    Linares is a Spanish football club known for being one of the early teams in the playing career of manager Rafael Benítez.
  • D. Lucena
    Lucena is a historic city in the province of Córdoba, Andalusia, southern Spain, known for its rich cultural heritage and former Jewish community.
  • E. 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.
  • 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_69d8b9ea20b48190ace88bb46b01e6a9 completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e47296770c8190b6b172fb647da564 completed April 19, 2026, 6:13 a.m.
Created at: April 10, 2026, 10:05 a.m.