Triple

T15716705
Position Surface form Disambiguated ID Type / Status
Subject Jeliza-Rose E380979 entity
Predicate givenName P17 FINISHED
Object Jeliza-Rose E380979 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: Jeliza-Rose | Statement: [Jeliza-Rose, givenName, Jeliza-Rose]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jeliza-Rose
Context triple: [Jeliza-Rose, givenName, Jeliza-Rose]
  • A. Jeliza-Rose chosen
    Jeliza-Rose is the imaginative and psychologically fragile young girl at the center of the dark fantasy film "Tideland," through whose perspective the story’s surreal and unsettling events unfold.
  • B. Aliza
    Aliza is a feminine given name, often considered a variant of Eliza and used in various cultures with meanings related to joy or nobility.
  • C. Julianna
    Julianna is a feminine given name most notably borne by American actress Julianna Margulies.
  • D. Lilia
    Lilia is a feminine given name, often considered a variant of Lily and associated with the elegance and symbolism of the lily flower.
  • E. Lizette
    Lizette is the nickname of American actress Elizabeth Rooney Mara, known for her roles in films like "The Girl with the Dragon Tattoo" and "Carol."
  • 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_69d86d9bf930819082b30cf6d169297c completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e04f91beb08190bd91bf9306737c3b completed April 16, 2026, 2:55 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff7583609c8190a80421fce649900f completed May 9, 2026, 5:57 p.m.
Created at: April 10, 2026, 4:45 a.m.