Triple

T4560124
Position Surface form Disambiguated ID Type / Status
Subject Drop Dead Gorgeous E120571 entity
Predicate editor P1954 FINISHED
Object Pamela Martin E458105 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: Pamela Martin | Statement: [Drop Dead Gorgeous, editor, Pamela Martin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pamela Martin
Context triple: [Drop Dead Gorgeous, editor, Pamela Martin]
  • A. Pamela Martin chosen
    Pamela Martin is an American film editor known for her work on acclaimed movies such as "The Fighter" and "Little Miss Sunshine."
  • B. Pamela Pettler
    Pamela Pettler is an American screenwriter best known for her work on darkly comedic animated films such as "Corpse Bride" and "Monster House."
  • C. Pamela Brown
    Pamela Brown was a British stage and film actress known for her intense character roles in mid-20th-century cinema and theatre.
  • D. Pamela Jones
    Pamela Jones is the fictional, somewhat overbearing but well-meaning mother of Bridget Jones in the "Bridget Jones" novels and film adaptations.
  • E. Pam Ferris
    Pam Ferris is a British actress known for her character roles in film and television, including memorable performances in "Matilda," "Call the Midwife," and "Harry Potter and the Prisoner of Azkaban."
  • 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_69bd4636f1648190a701445c2fcd9c17 completed March 20, 2026, 1:05 p.m.
NER Named-entity recognition batch_69bd582b871c8190be0b70c76d639000 completed March 20, 2026, 2:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7eea9112c8190b0f0040afbf916ce completed March 28, 2026, 3:07 p.m.
Created at: March 20, 2026, 1:09 p.m.