Triple

T4623320
Position Surface form Disambiguated ID Type / Status
Subject Fedora E101036 entity
Predicate stars P1956 FINISHED
Object Marthe Keller E213946 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: Marthe Keller | Statement: [Fedora, stars, Marthe Keller]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marthe Keller
Context triple: [Fedora, stars, Marthe Keller]
  • A. Marthe Keller chosen
    Marthe Keller is a Swiss actress and former opera director known for her international film career, including prominent roles in 1970s Hollywood thrillers and European cinema.
  • B. Marthe
    Marthe is a feminine given name, commonly used in French and other European languages, that is a variant of the name Martha.
  • C. Marguerite Gaudelet
    Marguerite Gaudelet was the wife of French civil engineer Gustave Eiffel, famed designer of the Eiffel Tower.
  • D. Marguerite Duthuit
    Marguerite Duthuit was a French woman best known as the daughter of painter Henri Matisse and the wife of art critic Georges Duthuit, placing her at the center of early 20th-century avant-garde artistic circles.
  • E. Mathilde Comont
    Mathilde Comont was a French-born character actress of the silent and early sound film era, known for her expressive performances in both European and Hollywood productions.
  • 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_69bd43d0497c8190ac23c65c5804846a completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd5a053d38819097b3ecbc06aa6e4d completed March 20, 2026, 2:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdfaa069388190b6482315708b85c2 completed March 21, 2026, 1:55 a.m.
Created at: March 20, 2026, 1:12 p.m.