Triple

T1911092
Position Surface form Disambiguated ID Type / Status
Subject Juan de Garay E38111 entity
Predicate birthPlace P1 FINISHED
Object Biscay E159064 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: Biscay | Statement: [Juan de Garay, birthPlace, Biscay]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Biscay
Context triple: [Juan de Garay, birthPlace, Biscay]
  • A. Biscay chosen
    Biscay is a coastal province in northern Spain, known for its capital Bilbao and its role as a historic and cultural center of the Basque Country.
  • B. Gipuzkoa
    Gipuzkoa is a coastal province in northern Spain known for its Basque culture, rugged landscapes, and the city of San Sebastián.
  • C. Navarre
    Navarre is an autonomous community and historical region in northern Spain known for its diverse landscapes, rich cultural traditions, and capital city of Pamplona.
  • D. Álava
    Álava is a province in northern Spain known for its capital Vitoria-Gasteiz and its role as one of the three historical territories of the Basque Country.
  • E. Ribera
    Ribera was a prominent 17th-century Spanish Baroque painter, known for his dramatic use of light and shadow and intense religious and genre scenes.
  • 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_69a8862a26088190aae5243695aeefc0 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb1b88db48190a9229a7416054a85 completed March 7, 2026, 5:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69adeaffbc2c81908303548fac82ff52 completed March 8, 2026, 9:32 p.m.
Created at: March 4, 2026, 7:35 p.m.