Triple

T10731069
Position Surface form Disambiguated ID Type / Status
Subject Yoo Soon-taek E253074 entity
Predicate honorificTitle P2097 FINISHED
Object Madame Ban E253074 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: Madame Ban | Statement: [Yoo Soon-taek, honorificTitle, Madame Ban]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Madame Ban
Context triple: [Yoo Soon-taek, honorificTitle, Madame Ban]
  • A. Madame Ban chosen
    Madame Ban is the honorific title commonly used for Yoo Soon-taek, the wife of former United Nations Secretary-General Ban Ki-moon and a prominent South Korean public figure.
  • B. Mrs. Boncassen
    Mrs. Boncassen is a fictional American matron from Anthony Trollope’s Palliser novels, known as the socially ambitious mother of Isabel Boncassen who navigates the complexities of British high society.
  • C. Blanchette
    Blanchette is a French feminine given name and diminutive form of Blanche, traditionally meaning "white" or "fair."
  • D. Madame Cama
    Madame Cama was an Indian independence activist and revolutionary who is best known for unfurling one of the first versions of the Indian national flag on foreign soil and advocating for India’s freedom from British rule.
  • E. Barbara
    Barbara is a station on Paris Métro Line 4 serving the southern suburbs of the French capital.
  • 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_69d6aa5d8be481909a43218b2bfdbe95 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d70fcb1cd881909635def59ad5d19c completed April 9, 2026, 2:32 a.m.
NED1 Entity disambiguation (via context triple) batch_69dbda1d1b108190a47b46661bc85b2d completed April 12, 2026, 5:45 p.m.
Created at: April 8, 2026, 9:14 p.m.