Triple

T23161489
Position Surface form Disambiguated ID Type / Status
Subject Monte Igueldo E578596 entity
Predicate nearbySettlement P350 FINISHED
Object San Sebastián 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: San Sebastián | Statement: [Monte Igueldo, nearbySettlement, San Sebastián]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: San Sebastián
Context triple: [Monte Igueldo, nearbySettlement, San Sebastián]
  • A. San Sebastián
    San Sebastián is a small town located within the Comayagua Department of central Honduras.
  • B. San Sebastián
    San Sebastián is a Guatemalan town located in the highlands of the San Marcos department, known for its proximity to Central America’s highest peak, Volcán Tajumulco.
  • C. San Sebastián
    San Sebastián is a district and urban area within the San José metropolitan region of Costa Rica, known for its residential neighborhoods and proximity to the country’s capital.
  • D. San Sebastián
    San Sebastián is a municipality located in the Retalhuleu Department of southwestern Guatemala, known for its agricultural activities and proximity to the Pacific coastal plain.
  • E. Donostia-San Sebastián chosen
    Donostia-San Sebastián is a coastal city in Spain’s Basque Country renowned for its picturesque bay, beaches, and world-class gastronomy.
  • 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_69e245fc75348190a0288401044c8af8 completed April 17, 2026, 2:38 p.m.
NER Named-entity recognition batch_69f18f006930819097aafef87405d737 completed April 29, 2026, 4:54 a.m.
Created at: April 17, 2026, 4:02 p.m.