Triple

T3836480
Position Surface form Disambiguated ID Type / Status
Subject Inspector Olivetti E91144 entity
Predicate createdBy P806 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: [Inspector Olivetti, createdBy, Dan Brown]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dan Brown
Context triple: [Inspector Olivetti, createdBy, 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. Anthony Horowitz
    Anthony Horowitz is a British novelist and screenwriter best known for his Alex Rider spy novels and numerous television crime dramas.
  • D. Frederick Forsyth
    Frederick Forsyth is a British thriller writer renowned for his meticulously researched, politically charged novels such as "The Day of the Jackal."
  • E. Robert Grace
    Robert Grace was an early American civic leader and philanthropist known for his role in colonial Philadelphia’s public institutions and community organizations.
  • 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_69aed960b538819096561c8ed448dec9 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeeb9baa508190800e73bf186f046e completed March 9, 2026, 3:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69b50405264c8190b145dc3929ffc940 completed March 14, 2026, 6:45 a.m.
Created at: March 9, 2026, 3:18 p.m.