Triple

T8628399
Position Surface form Disambiguated ID Type / Status
Subject Thor: Love and Thunder E204335 entity
Predicate portrayedBy P1507 FINISHED
Object Chris Hemsworth E197177 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: Chris Hemsworth | Statement: [Thor: Love and Thunder, portrayedBy, Chris Hemsworth]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chris Hemsworth
Context triple: [Thor: Love and Thunder, portrayedBy, Chris Hemsworth]
  • A. Chris Hemsworth chosen
    Chris Hemsworth is an Australian actor best known for portraying the Marvel superhero Thor in the Marvel Cinematic Universe films.
  • B. Hemsworth
    Hemsworth is a parliamentary constituency in West Yorkshire, England, represented in the UK House of Commons.
  • C. Tom Hiddleston
    Tom Hiddleston is an English actor best known for his charismatic portrayal of Loki in the Marvel Cinematic Universe and for acclaimed performances in film, television, and theatre.
  • D. Hunter Johansson
    Hunter Johansson is an American actor and activist best known as the twin brother of actress Scarlett Johansson.
  • E. James Norman Hiddleston
    James Norman Hiddleston is the father of British actor Tom Hiddleston and is known as a Scottish-born chemist and former managing director in the pharmaceutical industry.
  • 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_69ca834a4ea0819094970dceb9e389f3 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc473f6b888190ae40d65f24122c88 completed March 31, 2026, 10:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69cebc02b4008190a70ca8eb6f43926d completed April 2, 2026, 6:57 p.m.
Created at: March 30, 2026, 6:27 p.m.