Triple

T4197653
Position Surface form Disambiguated ID Type / Status
Subject Nick Clooney E85991 entity
Predicate spouse P13 FINISHED
Object Nina Bruce Warren E213738 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: Nina Bruce Warren | Statement: [Nick Clooney, spouse, Nina Bruce Warren]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nina Bruce Warren
Context triple: [Nick Clooney, spouse, Nina Bruce Warren]
  • A. Nina Bruce Warren chosen
    Nina Bruce Warren is an American woman best known as the mother of actor and filmmaker George Clooney.
  • B. Sarah Warren
    Sarah Warren was a daughter of Mayflower passenger Richard Warren, belonging to one of the early English settler families in colonial New England.
  • C. Anna Warren
    Anna Warren is a historical figure known primarily as a daughter of Mayflower passenger and Plymouth Colony settler Richard Warren.
  • D. Elizabeth Nourse
    Elizabeth Nourse was an American realist painter known for her sensitive depictions of women and rural life, who built a successful career in Paris in the late 19th and early 20th centuries.
  • E. Emily Warren
    Emily Warren was an American engineer and women’s rights advocate best known for her crucial role in overseeing the completion of the Brooklyn Bridge in the late 19th century.
  • 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_69aed93b89f48190a31f6d57c760e42f completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af0360bc8081908ceb2483eef89174 completed March 9, 2026, 5:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5d05b964881908d7d52b70cec2dcc completed March 14, 2026, 9:17 p.m.
Created at: March 9, 2026, 3:48 p.m.