Triple

T3407837
Position Surface form Disambiguated ID Type / Status
Subject Ivana Baquero E71817 entity
Predicate givenName P17 FINISHED
Object Ivana E258038 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: Ivana | Statement: [Ivana Baquero, givenName, Ivana]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ivana
Context triple: [Ivana Baquero, givenName, Ivana]
  • A. Ivana chosen
    Ivana is a feminine given name, common in Slavic countries, that is a variant of the name Joanna/John.
  • B. Ivana Marie Zelníčková
    Ivana Marie Zelníčková, better known as Ivana Trump, was a Czech-American businesswoman, former model, and the first wife of Donald Trump, noted for her role in his early real estate empire and her own fashion and lifestyle ventures.
  • C. Marija
    Marija is a feminine given name commonly used in Slavic and other European cultures, equivalent to "Maria" or "Mary."
  • D. Ivana Trump
    Ivana Trump was a Czech-American businesswoman, former model, and socialite best known as the first wife of Donald Trump and a prominent figure in New York high society in the 1980s and 1990s.
  • E. Ivana Baquero
    Ivana Baquero is a Spanish actress best known for her acclaimed performance as the young protagonist in Guillermo del Toro’s dark fantasy film "Pan’s Labyrinth."
  • 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_69ad85ac312481909e7027ced1456a9f completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb8ede9c48190b13b0f5e7474e7fa completed March 8, 2026, 5:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69b34bdaf06c8190a8102a4e3c728066 completed March 12, 2026, 11:27 p.m.
Created at: March 8, 2026, 3:15 p.m.