Triple

T12768875
Position Surface form Disambiguated ID Type / Status
Subject Morgan E305194 entity
Predicate editor P1954 FINISHED
Object Laura Jennings E248169 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: Laura Jennings | Statement: [Morgan, editor, Laura Jennings]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Laura Jennings
Context triple: [Morgan, editor, Laura Jennings]
  • A. Laura Jennings chosen
    Laura Jennings is a film editor best known for her work on major action and science fiction movies, including the Tom Cruise–led blockbuster "Edge of Tomorrow."
  • B. Laura Harrington
    Laura Harrington is an American actress best known for her role in the 1986 Stephen King film "Maximum Overdrive."
  • C. Laura Jarrett
    Laura Jarrett is an American attorney and journalist known for her work as a legal correspondent on major U.S. news networks.
  • D. Kate Jennings
    Kate Jennings was an Australian-born writer and poet known for her incisive feminist essays, political activism, and acclaimed novels exploring power, gender, and corporate culture.
  • E. Nessa Jenkins
    Nessa Jenkins is a deadpan, no-nonsense Welsh character from the British sitcom "Gavin & Stacey," known for her eccentric stories and iconic catchphrases.
  • 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_69d7bdf2b43c819098ae5aa68e61ea58 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d96df3b2f88190b37b696400178795 completed April 10, 2026, 9:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6cbb735e481909f120f95fa68f4f1 completed May 3, 2026, 4:14 a.m.
Created at: April 9, 2026, 5:28 p.m.