Triple

T8894726
Position Surface form Disambiguated ID Type / Status
Subject Dora Maar E211776 entity
Predicate givenName P17 FINISHED
Object Henriette E211775 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: Henriette | Statement: [Dora Maar, givenName, Henriette]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Henriette
Context triple: [Dora Maar, givenName, Henriette]
  • A. Henriette chosen
    Henriette is the given first name of the French photographer and painter Dora Maar, renowned for her association with Pablo Picasso and the Surrealist movement.
  • B. Henrietta
    Henrietta is a feminine given name of English origin, historically popular in the 18th and 19th centuries and borne by several notable figures.
  • C. Henrietta
    Henrietta is a suburban community in western New York State, located near Rochester within the Rust Belt region along the Interstate 90 corridor.
  • D. Marie Christine
    Marie Christine, better known as Princess Michael of Kent, is a member of the British royal family, an author, and the wife of Prince Michael of Kent, a first cousin of King Charles III.
  • E. Mariette
    Mariette is a French feminine given name, commonly used as a diminutive or affectionate form of Marie.
  • 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_69ca83918d3081909b326fa3750cb8c8 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc61be2c2081908f39cccdc149872d completed April 1, 2026, 12:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfba1e46a48190b7a559f9d9bd348d completed April 3, 2026, 1:01 p.m.
Created at: March 30, 2026, 6:54 p.m.