Triple

T10766727
Position Surface form Disambiguated ID Type / Status
Subject Calouste Gulbenkian E253972 entity
Predicate countryOfDeath P336 FINISHED
Object Portugal E866 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: Portugal | Statement: [Calouste Gulbenkian, countryOfDeath, Portugal]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Portugal
Context triple: [Calouste Gulbenkian, countryOfDeath, Portugal]
  • A. Portugal chosen
    Portugal is a Southern European country on the Iberian Peninsula, known for its maritime history, Atlantic coastline, and role as one of the world’s earliest global colonial powers.
  • B. Portogruaro
    Portogruaro is a historic town in northeastern Italy’s Veneto region, known for its medieval architecture and canals.
  • C. Portugal and Spain
    Portugal and Spain are neighboring Iberian countries in southwestern Europe known for their rich maritime histories, distinct Romance languages, and influential cultural and imperial legacies.
  • D. mainland Portugal
    Mainland Portugal is the continental part of the Portuguese Republic in southwestern Europe, comprising the country’s primary territory on the Iberian Peninsula.
  • E. Portela
    Portela is a residential parish in the municipality of Loures, within the Lisbon metropolitan area of Portugal.
  • 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_69d6aa5f54f4819082d0bbcb6f8797e6 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d7322d3a9c81909e58f6064643b814 completed April 9, 2026, 4:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69dbdb897f7c81909002f2478613eff8 completed April 12, 2026, 5:51 p.m.
Created at: April 8, 2026, 9:16 p.m.