Triple

T16708594
Position Surface form Disambiguated ID Type / Status
Subject Fernando Álvarez de Toledo, 3rd Duke of Alba E406037 entity
Predicate givenName P17 FINISHED
Object Fernando E406037 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: Fernando | Statement: [Fernando Álvarez de Toledo, 3rd Duke of Alba, givenName, Fernando]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fernando
Context triple: [Fernando Álvarez de Toledo, 3rd Duke of Alba, givenName, Fernando]
  • A. Fernando
    Fernando is the given name of Fernando Primo de Rivera, a 19th-century Spanish general and politician who briefly served as Prime Minister of Spain.
  • B. Fernando
    Fernando is a fictional character portrayed by actor Jake T. Austin, likely in a television or film role.
  • C. Fernando
    Fernando is the given name of Salgueiro Maia, a key Portuguese military officer who played a leading role in the Carnation Revolution.
  • D. Fernando chosen
    Fernando was the given name of the Duke of Alba who served as governor-general, a prominent Spanish noble and military leader.
  • E. Fernando
    Fernando is a masculine given name of Spanish and Portuguese origin, commonly used in many Spanish-speaking and Lusophone countries.
  • 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_69d8838db21081909589220fd71440a4 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e38650c3808190a561f22b169dc3ae completed April 18, 2026, 1:25 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0091a50eac81908af26a5131bf0a6c completed May 10, 2026, 2:09 p.m.
Created at: April 10, 2026, 5:20 a.m.