Triple

T12036506
Position Surface form Disambiguated ID Type / Status
Subject Nancy Wheeler E286552 entity
Predicate hasFriend P8712 FINISHED
Object Robin Buckley E277680 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: Robin Buckley | Statement: [Nancy Wheeler, hasFriend, Robin Buckley]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Robin Buckley
Context triple: [Nancy Wheeler, hasFriend, Robin Buckley]
  • A. Robin Buckley chosen
    Robin Buckley is a witty, sharp-tongued Hawkins teen and Steve Harrington’s close friend who works with him at Scoops Ahoy and later helps uncover sinister mysteries in the series Stranger Things.
  • B. Diane Buckley
    Diane Buckley is the central character of the sitcom "Trophy Wife," a former party girl adjusting to life as the third wife in a complicated blended family.
  • C. Ann Buck
    Ann Buck is known as the former wife of American sportscaster Joe Buck.
  • D. Gail Buckley
    Gail Buckley is an American author and historian known for her works on African American history and her family’s legacy, including being the daughter of singer and civil rights activist Lena Horne.
  • E. Laura Bickford
    Laura Bickford is an American film producer best known for her work on acclaimed independent and studio films, including the Oscar-winning drama "Traffic."
  • 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_69d6ab4669e48190b59246358b0383ab completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d90408cbf0819093270c9833ef149a completed April 10, 2026, 2:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69f60a613054819082f7900a6ba8fbf8 completed May 2, 2026, 2:29 p.m.
Created at: April 8, 2026, 9:47 p.m.