Triple

T14956603
Position Surface form Disambiguated ID Type / Status
Subject Mugatu E372945 entity
Predicate portrayedBy P1507 FINISHED
Object Will 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: Will Ferrell | Statement: [Mugatu, portrayedBy, Will Ferrell]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Will Ferrell
Context triple: [Mugatu, portrayedBy, Will Ferrell]
  • A. 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."
  • B. Will Forte
    Will Forte is an American actor, comedian, and writer best known for his work on "Saturday Night Live" and for creating and starring in the TV series "The Last Man on Earth."
  • C. Jon Heder
    Jon Heder is an American actor best known for his breakout role in the cult comedy film "Napoleon Dynamite" and subsequent performances in various comedy movies.
  • D. Owen Wilson
    Owen Wilson is an American actor and screenwriter known for his laid-back charm and roles in popular comedies and adventure films such as "Wedding Crashers," "Zoolander," and "Midnight in Paris."
  • E. Luke Wilson
    Luke Wilson is an American actor known for his roles in films such as "The Royal Tenenbaums," "Old School," and "Legally Blonde."
  • 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_69d85cca979481908747d2a81eba1cea completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded6cc73848190ac181782b20dc838 completed April 15, 2026, 12:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe8bccc26c8190bf571ea7aee0e0f6 completed May 9, 2026, 1:20 a.m.
Created at: April 10, 2026, 2:40 a.m.