Triple

T16146617
Position Surface form Disambiguated ID Type / Status
Subject Rene Russo as Kate Mullen E391800 entity
Predicate portrayedBy P1507 FINISHED
Object Rene Russo E207120 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: Rene Russo | Statement: [Rene Russo as Kate Mullen, portrayedBy, Rene Russo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rene Russo
Context triple: [Rene Russo as Kate Mullen, portrayedBy, Rene Russo]
  • A. Rene Russo chosen
    Rene Russo is an American actress and former model known for her roles in films such as "Lethal Weapon 3," "Outbreak," and "Nightcrawler."
  • B. Lee Russo
    Lee Russo is known as the spouse of American television host and film critic Ben Mankiewicz.
  • C. Laura Kugler
    Laura Kugler was the wife of Victor Kugler, one of the helpers who hid Anne Frank and her family during World War II.
  • D. Elizabeth Berkley
    Elizabeth Berkley is an American actress best known for her roles in the TV series "Saved by the Bell" and the film "Showgirls."
  • E. Diane Lane
    Diane Lane is an American actress acclaimed for her versatile performances in film and television, with a career spanning from childhood roles to major Hollywood productions.
  • 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_69d87f1c65e48190aa2b4c472e9bafc4 completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e21d947e68819081b4b7c757ce71b6 completed April 17, 2026, 11:46 a.m.
NED1 Entity disambiguation (via context triple) batch_6a007d9849a08190a575f19e816e6df2 completed May 10, 2026, 12:44 p.m.
Created at: April 10, 2026, 5:01 a.m.