Triple

T11760376
Position Surface form Disambiguated ID Type / Status
Subject Madam Secretary E279638 entity
Predicate character P662 FINISHED
Object Russell Jackson E289752 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: Russell Jackson | Statement: [Madam Secretary, character, Russell Jackson]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Russell Jackson
Context triple: [Madam Secretary, character, Russell Jackson]
  • A. Russell Jackson chosen
    Russell Jackson is a central fictional character in the political drama series "Madam Secretary," serving as the shrewd and pragmatic White House Chief of Staff.
  • B. Atley Jackson
    Atley Jackson is a character from the action film "Gone in 60 Seconds," involved in the high-stakes world of professional car theft.
  • C. Holton D. Robinson
    Holton D. Robinson was an American civil engineer noted for his work on major suspension bridges in the early 20th century.
  • D. Joseph Burkett
    Joseph Burkett is an American defense contractor best known as the husband of journalist and war correspondent Lara Logan.
  • E. Noble Johnson
    Noble Johnson was an American character actor and pioneering African-American film producer known for his prolific work in early Hollywood cinema, including roles in classic adventure and horror films.
  • 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_69d6ab01038c819080714901502c84fc completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a52386708190b744746a2db37495 completed April 10, 2026, 7:22 a.m.
NED1 Entity disambiguation (via context triple) batch_69f4713ee6e48190ab1860b9899b7b48 completed May 1, 2026, 9:24 a.m.
Created at: April 8, 2026, 9:41 p.m.