Triple

T19835785
Position Surface form Disambiguated ID Type / Status
Subject Taylor Russell E476590 entity
Predicate name P16 FINISHED
Object Taylor Russell 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: Taylor Russell | Statement: [Taylor Russell, name, Taylor Russell]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Taylor Russell
Context triple: [Taylor Russell, name, Taylor Russell]
  • A. Taylor Russell chosen
    Taylor Russell is a Canadian actress best known for her breakout role in the Netflix sci-fi series "Lost in Space" and acclaimed performances in films such as "Waves" and "Bones and All."
  • B. Tracee Joy Silberstein
    Tracee Joy Silberstein is the birth name of Tracee Ellis Ross, an American actress and television host best known for her roles on the sitcoms "Girlfriends" and "Black-ish."
  • C. Clover Russell
    Clover Russell was the wife of British intelligence officer and author Edward Wilson, associated with his personal and professional life.
  • D. Haley Bennett
    Haley Bennett is an American actress and singer known for her versatile performances in films such as "The Girl on the Train," "The Magnificent Seven," and "Swallow."
  • E. Kyliegh Curran
    Kyliegh Curran is an American actress best known for her roles in the horror film "Doctor Sleep" and the Netflix series "The Fall of the House of Usher."
  • 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_69d8e51c7c188190b926f3a2a7b5f881 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e656d275608190841b23de167c401e completed April 20, 2026, 4:39 p.m.
Created at: April 10, 2026, 1:50 p.m.