Triple

T23072330
Position Surface form Disambiguated ID Type / Status
Subject Maria Teresa Thierstein Simões-Ferreira E575229 entity
Predicate spouse P13 FINISHED
Object John Kerry NE NERFINISHED

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: John Kerry | Statement: [Maria Teresa Thierstein Simões-Ferreira, spouse, John Kerry]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John Kerry
Context triple: [Maria Teresa Thierstein Simões-Ferreira, spouse, John Kerry]
  • A. John Kerry chosen
    John Kerry is an American politician and diplomat, former U.S. senator from Massachusetts, 2004 Democratic presidential nominee, former Secretary of State, and a leading figure in U.S. foreign policy and climate diplomacy.
  • B. Albert Kerry
    Albert Kerry was a local Seattle businessman and civic leader after whom the scenic Kerry Park viewpoint on Queen Anne Hill is named.
  • C. Kerry
    Kerry is a county in the southwest of Ireland known for its rugged coastline, mountains, and popular tourist destinations like Killarney and the Ring of Kerry.
  • D. Kerry
    Kerry is a given name used for people of any gender in English-speaking countries.
  • E. Ted Kennedy
    Ted Kennedy was a Canadian Hall of Fame ice hockey centre best known as a longtime star and captain of the Toronto Maple Leafs in the NHL.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e245be28d48190ad1348d5a73db37d completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f18c5ffad881909ed626045c15dd33 completed April 29, 2026, 4:43 a.m.
Created at: April 17, 2026, 3:56 p.m.