Triple

T3819989
Position Surface form Disambiguated ID Type / Status
Subject Ransom E84348 entity
Predicate starring 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: [Ransom, starring, Rene Russo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rene Russo
Context triple: [Ransom, starring, 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. 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."
  • C. 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.
  • D. Lea Thompson
    Lea Thompson is an American actress best known for her role as Lorraine Baines McFly in the Back to the Future film trilogy.
  • E. Andie MacDowell
    Andie MacDowell is an American actress and former fashion model best known for her roles in romantic comedies such as "Groundhog Day" and "Four Weddings and a Funeral."
  • 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_69aed931f5908190be2c07af66d4df25 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeea601d408190b09dc486e77488d4 completed March 9, 2026, 3:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69b503f75f6c8190b9af77ed212a2774 completed March 14, 2026, 6:45 a.m.
Created at: March 9, 2026, 3:17 p.m.