Triple

T9351663
Position Surface form Disambiguated ID Type / Status
Subject Wanda Krahelska-Filipowicz E225032 entity
Predicate givenName P17 FINISHED
Object Wanda E253732 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: Wanda | Statement: [Wanda Krahelska-Filipowicz, givenName, Wanda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wanda
Context triple: [Wanda Krahelska-Filipowicz, givenName, Wanda]
  • A. Wanda chosen
    Wanda is a feminine given name of Slavic origin, particularly common in Poland and other Central and Eastern European countries.
  • B. Wanda
    Wanda is a fairy godparent character from the animated series "The Fairly OddParents," known for her responsible and level-headed personality.
  • C. Scarlet Witch
    Scarlet Witch is a powerful Marvel Comics superhero and Avenger, known for her reality-warping chaos magic and complex moral journey.
  • D. Luna Maximoff
    Luna Maximoff is a Marvel Comics character, the daughter of Quicksilver and Crystal, notable as a human-Inhuman hybrid with empathic abilities.
  • E. Margareta
    Margareta is a feminine given name used in various European languages, closely related to and derived from the name Margaret.
  • 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_69ca842abfd48190949d71c3b86eeba8 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd4f93a9848190ad2ae24f2aa607d2 completed April 1, 2026, 5:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69d0e445c5308190be122215c92fd03d completed April 4, 2026, 10:13 a.m.
Created at: March 30, 2026, 7:41 p.m.