Triple

T6113081
Position Surface form Disambiguated ID Type / Status
Subject Madame Élisabeth of France E136292 entity
Predicate givenName P17 FINISHED
Object Élisabeth E113408 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: Élisabeth | Statement: [Madame Élisabeth of France, givenName, Élisabeth]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Élisabeth
Context triple: [Madame Élisabeth of France, givenName, Élisabeth]
  • A. Elisabeth chosen
    Elisabeth is a feminine given name of Hebrew origin, commonly used in various European languages as a form of Elizabeth.
  • B. Elisabeth
    Elisabeth is a metro station on the Brussels Metro system in Brussels, Belgium.
  • C. Victoria of France
    Victoria of France was a French princess, daughter of King Henry II and Catherine de' Medici, and sister of King Francis II of France.
  • D. Elisabeth of France
    Elisabeth of France was a 17th-century French princess of the Bourbon dynasty who became Queen of Spain and Portugal as the first wife of King Philip IV.
  • E. Eléonore Fabiola Victoria Anne
    Eléonore Fabiola Victoria Anne is the youngest child of King Philippe and Queen Mathilde of Belgium and a princess of the Belgian royal family.
  • 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_69c0089ea6f88190b349be53e04b4f5f completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c05bbf2ee4819097af2cce9248bf4e completed March 22, 2026, 9:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69c603d90bcc81908f819a9e262ec17a completed March 27, 2026, 4:13 a.m.
Created at: March 22, 2026, 4:14 p.m.