Triple

T12969735
Position Surface form Disambiguated ID Type / Status
Subject Nadine Tolliver E321360 entity
Predicate creator P184 FINISHED
Object Barbara Hall E513537 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: Barbara Hall | Statement: [Nadine Tolliver, creator, Barbara Hall]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Barbara Hall
Context triple: [Nadine Tolliver, creator, Barbara Hall]
  • A. Barbara Hall chosen
    Barbara Hall is an American television writer and producer best known for creating series such as "Madam Secretary" and "Joan of Arcadia."
  • B. Barbara Hall
    Barbara Hall is a Canadian politician who served as the 60th mayor of Toronto in the 1990s and later became Ontario's chief commissioner of human rights.
  • C. Barbara M. Rolph
    Barbara M. Rolph was the woman who sponsored the U.S. Navy heavy cruiser USS San Francisco (CA-38) at its launching ceremony.
  • D. Barbara Richardson
    Barbara Richardson is an American public figure best known as the longtime wife and partner of the late New Mexico governor and U.S. diplomat Bill Richardson, with whom she was active in civic and charitable causes.
  • E. Margaret Haller
    Margaret Haller is an American author best known for her books on etiquette and social behavior.
  • 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_69d80763bd6c819094437da5b20b01d2 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97e407e5081909424fc0c22483c28 completed April 10, 2026, 10:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00679214208190a9ee4cce882f59cb completed May 10, 2026, 11:10 a.m.
Created at: April 9, 2026, 8:34 p.m.