Triple

T22760794
Position Surface form Disambiguated ID Type / Status
Subject Evelyn E562980 entity
Predicate starring P1507 FINISHED
Object Julianna Margulies 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: Julianna Margulies | Statement: [Evelyn, starring, Julianna Margulies]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Julianna Margulies
Context triple: [Evelyn, starring, Julianna Margulies]
  • A. Julianna Margulies chosen
    Julianna Margulies is an American actress best known for her acclaimed television roles on series such as "ER" and "The Good Wife."
  • B. Laura Birn
    Laura Birn is a Finnish actress known internationally for her role in the science fiction television series "Foundation" and for her work in both Finnish cinema and global productions.
  • C. Katherine Noel Brosnahan
    Katherine Noel Brosnahan, better known as Kate Spade, was an American fashion designer and businesswoman who co-founded the iconic accessories and lifestyle brand Kate Spade New York.
  • D. Claire Danes
    Claire Danes is an American actress acclaimed for her roles in projects such as the television series "Homeland" and the film "Romeo + Juliet."
  • E. Betty Gilpin
    Betty Gilpin is an American actress best known for her Emmy-nominated role in the Netflix series "GLOW" and performances in films such as "The Hunt" and "The Tomorrow War."
  • 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_69e24552e11c81909c2d61578a558bd7 completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f17a7c45b881908b29ba1439038789 completed April 29, 2026, 3:26 a.m.
Created at: April 17, 2026, 3:26 p.m.