Triple

T10155283
Position Surface form Disambiguated ID Type / Status
Subject Elisabeta E232759 entity
Predicate isCognateOf P2527 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: [Elisabeta, isCognateOf, Élisabeth]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Élisabeth
Context triple: [Elisabeta, isCognateOf, Élisabeth]
  • A. Elisabeth
    Elisabeth is a metro station on the Brussels Metro system in Brussels, Belgium.
  • B. Elisabeth chosen
    Elisabeth is a feminine given name of Hebrew origin, commonly used in various European languages as a form of Elizabeth.
  • 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. Élisabeth of France
    Élisabeth of France was an 18th-century French princess, daughter of King Louis XV and sister of King Louis XVI, known for her piety and loyalty to the royal family during the French Revolution.
  • E. Élisabeth Alexandrine de Bourbon
    Élisabeth Alexandrine de Bourbon was an 18th-century French princess of the blood from the House of Bourbon, known for her position at the court of Louis XV and her substantial wealth and patronage.
  • 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_69ca84885e48819088a31b127cf44904 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cdec3a5e7c819098b2f9ccbde7cf94 completed April 2, 2026, 4:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69d354fc0a808190aa57b68708d91590 completed April 6, 2026, 6:38 a.m.
Created at: March 30, 2026, 9:09 p.m.