Triple

T9656964
Position Surface form Disambiguated ID Type / Status
Subject Hawkins E233482 entity
Predicate hasNotableBearer P458 FINISHED
Object Paula Hawkins E126652 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: Paula Hawkins | Statement: [Hawkins, hasNotableBearer, Paula Hawkins]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Paula Hawkins
Context triple: [Hawkins, hasNotableBearer, Paula Hawkins]
  • A. Paula Hawkins chosen
    Paula Hawkins is a British author best known for her psychological thriller novel "The Girl on the Train," which was adapted into the 2016 film of the same name.
  • B. Val McDermid
    Val McDermid is a Scottish crime writer renowned for her psychological thrillers and influential contributions to contemporary crime fiction.
  • C. Susan Hill
    Susan Hill is a British author best known for her ghost stories and novels, including the modern classic "The Woman in Black."
  • D. Kate Morton
    Kate Morton is an Australian bestselling novelist known for her atmospheric historical mysteries such as "The Forgotten Garden" and "The House at Riverton."
  • E. Lisa Gardner
    Lisa Gardner is an American author best known for her bestselling crime and psychological thriller novels, including the Detective D.D. Warren and FBI Profiler series.
  • 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_69ca848c1ba88190b84b410cd14627fc completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9bdd5c0c8190a6c82a1609454d1b completed April 1, 2026, 10:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69d18a07444c819099d7462c38f6da49 completed April 4, 2026, 10 p.m.
Created at: March 30, 2026, 8:14 p.m.