Triple

T2644872
Position Surface form Disambiguated ID Type / Status
Subject A Little Princess (1995 film) E62959 entity
Predicate character P662 FINISHED
Object Becky E38129 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: Becky | Statement: [A Little Princess (1995 film), character, Becky]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Becky
Context triple: [A Little Princess (1995 film), character, Becky]
  • A. Becky chosen
    Becky is a common English feminine given name, typically used as a diminutive of Rebecca.
  • B. Bella Higginbotham
    Bella Higginbotham is an American actress best known for her role in the film "Troop Zero" and for appearing in various television and streaming series.
  • C. Rebeca
    Rebeca is a feminine given name, commonly used in Spanish- and Portuguese-speaking countries, that is a variant of the name Rebecca.
  • D. Lauren
    Lauren is a central female protagonist in the romantic comedy film "Think Like a Man," portrayed as a successful, relationship-seeking woman whose love life is influenced by Steve Harvey’s dating advice.
  • E. Julie Beckman
    Julie Beckman is an American architect best known for co-designing the National 9/11 Pentagon Memorial in Arlington, Virginia.
  • 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_69ab4c3f2dcc819082df80f5e032f690 completed March 6, 2026, 9:50 p.m.
NER Named-entity recognition batch_69abd917192081908e7a2cf780a17b83 completed March 7, 2026, 7:51 a.m.
NED1 Entity disambiguation (via context triple) batch_69af98c50d108190b716dd51c34d3759 completed March 10, 2026, 4:06 a.m.
Created at: March 6, 2026, 9:53 p.m.