Triple

T7614012
Position Surface form Disambiguated ID Type / Status
Subject Constance Frances Marie Ockelman E172315 entity
Predicate alsoKnownAs P39 FINISHED
Object Veronica Lake E31920 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: Veronica Lake | Statement: [Constance Frances Marie Ockelman, alsoKnownAs, Veronica Lake]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Veronica Lake
Context triple: [Constance Frances Marie Ockelman, alsoKnownAs, Veronica Lake]
  • A. Veronica Lake chosen
    Veronica Lake was a popular American film actress of the 1940s, famed for her roles in film noir and her iconic peek-a-boo hairstyle.
  • B. Volga Hayworth
    Volga Hayworth was the mother of Hollywood actress Rita Hayworth and part of the family background that shaped the star's early life.
  • C. Barbara La Marr
    Barbara La Marr was a popular American silent film actress and screenwriter of the early 1920s, often billed as "The Girl Who Is Too Beautiful."
  • D. Jo Harlow
    Jo Harlow is a technology executive best known for leading mobile device and smartphone businesses at companies such as Nokia and later Microsoft.
  • E. Jean Harlow
    Jean Harlow was a legendary American film actress and 1930s sex symbol known for her platinum blonde image and starring roles in early Hollywood comedies and dramas.
  • 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_69c6994f50808190ba228764bb422417 completed March 27, 2026, 2:50 p.m.
NER Named-entity recognition batch_69c6fa418ef081908fd17ff367520995 completed March 27, 2026, 9:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69c86868dce08190b31229ff2e06fe0c completed March 28, 2026, 11:46 p.m.
Created at: March 27, 2026, 3:55 p.m.