Triple

T18676750
Position Surface form Disambiguated ID Type / Status
Subject Christina Aguilera E456620 entity
Predicate notableWork P4 FINISHED
Object What a Girl Wants 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: What a Girl Wants | Statement: [Christina Aguilera, notableWork, What a Girl Wants]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: What a Girl Wants
Context triple: [Christina Aguilera, notableWork, What a Girl Wants]
  • A. What a Girl Wants chosen
    "What a Girl Wants" is a pop and R&B song by Christina Aguilera that became one of her early signature hits around the turn of the millennium.
  • B. What a Girl Wants
    "What a Girl Wants" is a 2003 romantic comedy film starring Amanda Bynes as an American teenager who travels to London to meet her aristocratic father for the first time.
  • C. What a Woman Wants
    "What a Woman Wants" is a musical number from the stage adaptation of the film *Kinky Boots*, featuring lyrics by Cyndi Lauper that explores themes of desire, identity, and empowerment.
  • D. She's All That
    "She's All That" is a 1999 teen romantic comedy film about a high school jock who bets he can transform an unpopular girl into the prom queen.
  • E. Getting the Girl
    Getting the Girl is a young adult novel by Markus Zusak that follows a teenage boy navigating love, friendship, and identity in suburban Australia.
  • 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_69d8d38f72b4819090a935175d9ca8af completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e556b5a52c81908a71ac86544fb6aa completed April 19, 2026, 10:27 p.m.
Created at: April 10, 2026, 11:48 a.m.