Triple

T21271599
Position Surface form Disambiguated ID Type / Status
Subject Princess Antoinette of Saxe-Coburg-Saalfeld E524272 entity
Predicate givenName P17 FINISHED
Object Antoinette 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: Antoinette | Statement: [Princess Antoinette of Saxe-Coburg-Saalfeld, givenName, Antoinette]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Antoinette
Context triple: [Princess Antoinette of Saxe-Coburg-Saalfeld, givenName, Antoinette]
  • A. Antoinette chosen
    Antoinette is a feminine given name of French origin, historically associated with nobility and later borne by various notable figures in the arts and public life.
  • B. Antoinette
    Antoinette is the birth name of Princess Muna al-Hussein, the British-born mother of King Abdullah II of Jordan.
  • C. Antoinette
    Antoinette is an American hip hop artist known for her late-1980s and early-1990s recordings, including work released through Next Plateau Records.
  • D. Lucienne
    Lucienne is a feminine given name of French origin, traditionally used in Francophone countries.
  • E. Renée
    Renée is a feminine given name of French origin, commonly used in French-speaking countries and beyond.
  • 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_69e0b516293c819089458ea2ec85f85e completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e736534c348190a8d29e8d724dd40a completed April 21, 2026, 8:33 a.m.
Created at: April 16, 2026, 4:01 p.m.