Triple

T4716692
Position Surface form Disambiguated ID Type / Status
Subject John William Ferrell E104657 entity
Predicate hasFamilyName P18 FINISHED
Object Ferrell E12114 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: Ferrell | Statement: [John William Ferrell, hasFamilyName, Ferrell]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ferrell
Context triple: [John William Ferrell, hasFamilyName, Ferrell]
  • A. Magnus Paulin Ferrell
    Magnus Paulin Ferrell is the son of Swedish actress Viveca Paulin and American comedian and actor Will Ferrell.
  • B. Will Ferrell chosen
    Will Ferrell is an American comedian, actor, writer, and producer best known for his work on "Saturday Night Live" and a series of hit comedy films such as "Anchorman" and "Elf."
  • C. Rob Riggle
    Rob Riggle is an American actor, comedian, and former Marine officer known for his energetic, often over-the-top roles in film and television comedies.
  • D. David Koechner
    David Koechner is an American character actor and comedian best known for his scene-stealing roles in films like Anchorman and the TV series The Office.
  • E. John Ferrell
    John Ferrell was a photographer for the U.S. Farm Security Administration, contributing documentary images of American life during the Great Depression and World War II era.
  • 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_69bd43ec4a348190bc41afae43375e71 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd640a32ec8190850146957885c3cf completed March 20, 2026, 3:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69be39f7316c8190a6ecd65b707d3fbe completed March 21, 2026, 6:25 a.m.
Created at: March 20, 2026, 1:18 p.m.