Triple

T3191868
Position Surface form Disambiguated ID Type / Status
Subject Lee Pace E66840 entity
Predicate hasWorkedWith P9615 FINISHED
Object Evangeline Lilly E256908 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: Evangeline Lilly | Statement: [Lee Pace, hasWorkedWith, Evangeline Lilly]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Evangeline Lilly
Context triple: [Lee Pace, hasWorkedWith, Evangeline Lilly]
  • A. Evangeline Lilly chosen
    Evangeline Lilly is a Canadian actress best known for her roles in the television series "Lost" and as Hope van Dyne/The Wasp in the Marvel Cinematic Universe.
  • B. Kate Beckinsale
    Kate Beckinsale is an English actress known for her versatile film career, including prominent roles in action, drama, and comedy films such as the Underworld series and various Hollywood productions.
  • C. Shannon Elizabeth
    Shannon Elizabeth is an American actress and former fashion model best known for her breakout role in the comedy film "American Pie."
  • D. Ali Larter
    Ali Larter is an American actress and former model best known for her roles in films like "Final Destination" and "Legally Blonde" and the TV series "Heroes."
  • E. Teresa Palmer
    Teresa Palmer is an Australian actress known for her roles in films such as "Warm Bodies," "Lights Out," and "Hacksaw Ridge."
  • 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_69ad8588ba18819086a10951c32ecb80 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada6e8e4b48190bc7c6443fc6da900 completed March 8, 2026, 4:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69b24ba19b2881908a298e6fb99a058e completed March 12, 2026, 5:14 a.m.
Created at: March 8, 2026, 3:07 p.m.