Triple

T8433653
Position Surface form Disambiguated ID Type / Status
Subject Maria Hill E199173 entity
Predicate portrayedBy P1507 FINISHED
Object Cobie Smulders E213245 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: Cobie Smulders | Statement: [Maria Hill, portrayedBy, Cobie Smulders]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cobie Smulders
Context triple: [Maria Hill, portrayedBy, Cobie Smulders]
  • A. Cobie Smulders chosen
    Cobie Smulders is a Canadian actress best known for her role as Robin Scherbatsky on the sitcom "How I Met Your Mother" and for portraying Maria Hill in the Marvel Cinematic Universe.
  • B. Erika Christensen
    Erika Christensen is an American actress known for her roles in films like "Traffic" and "Flightplan" and the television series "Parenthood."
  • C. Kristin Davis
    Kristin Davis is an American actress best known for her role as Charlotte York on the television series "Sex and the City" and its related films.
  • D. Judy Greer
    Judy Greer is an American actress known for her versatile supporting roles in film and television, including appearances in major franchises like the Marvel Cinematic Universe.
  • E. Julianna Margulies
    Julianna Margulies is an American actress best known for her acclaimed television roles on series such as "ER" and "The Good Wife."
  • 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_69ca8314cd6c8190a6b8c2a1096e18f3 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbd1a74d948190abd76e7a6efb42ec completed March 31, 2026, 1:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce1d71d9748190903ed97dde6d28f4 completed April 2, 2026, 7:40 a.m.
Created at: March 30, 2026, 6:07 p.m.