Triple

T10607406
Position Surface form Disambiguated ID Type / Status
Subject Elizabeth Marvel as Jean Meyerowitz E275910 entity
Predicate portrayedBy P1507 FINISHED
Object Elizabeth Marvel E272605 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: Elizabeth Marvel | Statement: [Elizabeth Marvel as Jean Meyerowitz, portrayedBy, Elizabeth Marvel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elizabeth Marvel
Context triple: [Elizabeth Marvel as Jean Meyerowitz, portrayedBy, Elizabeth Marvel]
  • A. Elizabeth Marvel chosen
    Elizabeth Marvel is an American actress known for her versatile performances in film, television, and theater, including prominent roles in series like Homeland and House of Cards.
  • B. Amy Ruck
    Amy Ruck was the wife of botanist Francis Darwin, son of Charles Darwin, and a member of the extended Darwin family circle in late 19th-century England.
  • C. Melissa Cobb
    Melissa Cobb is an American film producer best known for her work on major animated features, including the Kung Fu Panda franchise.
  • D. Beth Riesgraf
    Beth Riesgraf is an American actress best known for playing the quirky thief Parker on the television series "Leverage."
  • E. Lynn Collins
    Lynn Collins is an American actress known for her roles in films such as X-Men Origins: Wolverine and John Carter, as well as various television series.
  • 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_69d6aaf948d88190806cc3a8c47a3fb2 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d6df4c38c881908f69bb757b8e03f5 completed April 8, 2026, 11:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69dff76eef4c8190b4fe681a9431207d completed April 15, 2026, 8:39 p.m.
Created at: April 8, 2026, 7:32 p.m.