Triple

T14456185
Position Surface form Disambiguated ID Type / Status
Subject Priory of Sion E358464 entity
Predicate popularizedBy P4586 FINISHED
Object Dan Brown E72333 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: Dan Brown | Statement: [Priory of Sion, popularizedBy, Dan Brown]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dan Brown
Context triple: [Priory of Sion, popularizedBy, Dan Brown]
  • A. Dan Brown chosen
    Dan Brown is an American author best known for his fast-paced mystery thrillers that blend historical, religious, and conspiracy themes, including the bestselling novel "The Da Vinci Code."
  • B. Robert Ludlum
    Robert Ludlum was an American author best known for his fast-paced espionage and thriller novels, including the Jason Bourne series.
  • C. Nicholas Evans
    Nicholas Evans was a British author best known for his bestselling novel "The Horse Whisperer," which was adapted into a major film.
  • D. Anthony Horowitz
    Anthony Horowitz is a British novelist and screenwriter best known for his Alex Rider spy novels and numerous television crime dramas.
  • E. Jonathan Harr
    Jonathan Harr is an American author and journalist best known for his nonfiction legal thriller "A Civil Action," which chronicles a landmark environmental lawsuit.
  • 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_69d82794dfa081909b9134ad2e32244b completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de91a9c0d48190ae015e5e0db806ca completed April 14, 2026, 7:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd649177108190be32af72dcae04ee completed May 8, 2026, 4:20 a.m.
Created at: April 10, 2026, 1:19 a.m.