Triple

T13121784
Position Surface form Disambiguated ID Type / Status
Subject Lucienne Michaux-Chevry Mollet E311740 entity
Predicate givenName P17 FINISHED
Object Lucienne
Lucienne is a feminine given name of French origin, traditionally used in Francophone countries.
 E1026097 NE FINISHED

How this triple was built (4 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: Lucienne | Statement: [Lucienne Michaux-Chevry Mollet, givenName, Lucienne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lucienne
Context triple: [Lucienne Michaux-Chevry Mollet, givenName, Lucienne]
  • A. Liliane
    Liliane is a feminine given name of French origin, notably borne by French heiress and businesswoman Liliane Bettencourt.
  • B. Laetitia
    Laetitia is a feminine given name of Latin origin, historically borne by figures such as the English poet and essayist Anna Laetitia Barbauld.
  • C. Antoinette
    Antoinette is the birth name of Princess Muna al-Hussein, the British-born mother of King Abdullah II of Jordan.
  • D. Antoinette
    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.
  • E. Lucile
    Lucile is a popular 1860 verse novel by British writer Edward Bulwer-Lytton, known for its romantic plot and melodramatic style.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Lucienne
Triple: [Lucienne Michaux-Chevry Mollet, givenName, Lucienne]
Generated description
Lucienne is a feminine given name of French origin, traditionally used in Francophone countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lucienne
Target entity description: Lucienne is a feminine given name of French origin, traditionally used in Francophone countries.
  • A. Liliane
    Liliane is a feminine given name of French origin, notably borne by French heiress and businesswoman Liliane Bettencourt.
  • B. Laetitia
    Laetitia is a feminine given name of Latin origin, historically borne by figures such as the English poet and essayist Anna Laetitia Barbauld.
  • C. Antoinette
    Antoinette is the birth name of Princess Muna al-Hussein, the British-born mother of King Abdullah II of Jordan.
  • D. Antoinette
    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.
  • E. Lucile
    Lucile is a popular 1860 verse novel by British writer Edward Bulwer-Lytton, known for its romantic plot and melodramatic style.
  • F. None of above. chosen

Provenance (5 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_69d806a9fe888190b081e2d9ea665d6c completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d9819840b881909b76022b4c4dcaed completed April 10, 2026, 11:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6f5d205408190883b67739d5efaa7 completed May 3, 2026, 7:14 a.m.
NEDg Description generation batch_69f6f6e10f2481909b405169dd7e5cf9 completed May 3, 2026, 7:18 a.m.
NED2 Entity disambiguation (via description) batch_69f6f73b301881909d792dfebd2e468f completed May 3, 2026, 7:20 a.m.
Created at: April 9, 2026, 9:06 p.m.