Triple

T21081766
Position Surface form Disambiguated ID Type / Status
Subject Wójcicki E519386 entity
Predicate hasFeminineForm P1613 FINISHED
Object Wójcicka NE NERFINISHED

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: Wójcicka | Statement: [Wójcicki, hasFeminineForm, Wójcicka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wójcicka
Context triple: [Wójcicki, hasFeminineForm, Wójcicka]
  • A. Agnieszka Włodarczyk
    Agnieszka Włodarczyk is a Polish actress and singer best known for her roles in popular Polish films and television series.
  • B. Śliwińska
    Śliwińska is a Polish surname most notably borne by professional ballroom dancer and choreographer Edyta Śliwińska.
  • C. Marta Kwiatkowska
    Marta Kwiatkowska is a prominent computer scientist known for her contributions to probabilistic model checking and formal verification.
  • D. Walewska
    Walewska is a Polish surname most famously associated with Maria Walewska, a noblewoman known as the mistress of Napoleon Bonaparte.
  • E. Wójcicki chosen
    Wójcicki is a Polish surname most notably associated with figures such as Susan Wojcicki, the former CEO of YouTube, and her family.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b506e59c8190849b71ed07929215 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e702db430c81908a1547d8fbe45506 completed April 21, 2026, 4:53 a.m.
Created at: April 16, 2026, 2:49 p.m.