Triple

T3749487
Position Surface form Disambiguated ID Type / Status
Subject Queen Sofía of Spain E81292 entity
Predicate givenName P17 FINISHED
Object Sofía E173123 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: Sofía | Statement: [Queen Sofía of Spain, givenName, Sofía]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sofía
Context triple: [Queen Sofía of Spain, givenName, Sofía]
  • A. Belén
    Belén is a town in northwestern Argentina known for its traditional weaving and role as a regional center in Catamarca Province.
  • B. Sofia
    Sofia is a strong-willed, outspoken woman in Alice Walker’s "The Color Purple," known for her resilience and defiance against oppression.
  • C. Sofia
    Sofia is the capital and largest city of Bulgaria, known as a major cultural, economic, and historical center in the Balkans.
  • D. Sofia chosen
    Sofia is a feminine given name of Greek origin, widely used in many cultures and commonly associated with the meaning "wisdom."
  • E. Risca
    Risca is a town in south Wales situated in the county borough of Caerphilly, near Newport, with a history rooted in coal mining and industry.
  • 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_69ad8b19b7b08190a6188804e99c53e9 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcb6bf95c81909796fbc84995ae05 completed March 8, 2026, 7:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4db31f964819087bab143f638754f completed March 14, 2026, 3:51 a.m.
Created at: March 8, 2026, 3:35 p.m.