Triple

T5169084
Position Surface form Disambiguated ID Type / Status
Subject Armande Béjart E116630 entity
Predicate givenName P17 FINISHED
Object Armande
Armande is a French given name historically associated with figures in the performing arts, notably in 17th-century France.
 E507696 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: Armande | Statement: [Armande Béjart, givenName, Armande]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Armande
Context triple: [Armande Béjart, givenName, Armande]
  • A. Françoise
    Françoise is the given name of Louise de La Vallière, a 17th-century French noblewoman best known as a mistress of King Louis XIV.
  • 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. Renée
    Renée is a feminine given name of French origin, commonly used in French-speaking countries and beyond.
  • D. Charlène
    Charlène is a French feminine given name, typically considered a variant of Charlene or a diminutive of Charlotte.
  • E. Antoinette
    Antoinette is the birth name of Princess Muna al-Hussein, the British-born mother of King Abdullah II of Jordan.
  • 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: Armande
Triple: [Armande Béjart, givenName, Armande]
Generated description
Armande is a French given name historically associated with figures in the performing arts, notably in 17th-century France.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Armande
Target entity description: Armande is a French given name historically associated with figures in the performing arts, notably in 17th-century France.
  • A. Françoise
    Françoise is the given name of Louise de La Vallière, a 17th-century French noblewoman best known as a mistress of King Louis XIV.
  • 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. Renée
    Renée is a feminine given name of French origin, commonly used in French-speaking countries and beyond.
  • D. Charlène
    Charlène is a French feminine given name, typically considered a variant of Charlene or a diminutive of Charlotte.
  • E. 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.
  • 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_69bd445ff97c81909a2615cc56235470 completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd794dd9988190922e138f2a9a3c62 completed March 20, 2026, 4:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf06ac60448190a2e97a4df03863ea completed March 21, 2026, 8:59 p.m.
NEDg Description generation batch_69bf07c20ad48190a74baf3b073850df completed March 21, 2026, 9:04 p.m.
NED2 Entity disambiguation (via description) batch_69bf0810acf481909a61b9db9d09a414 completed March 21, 2026, 9:05 p.m.
Created at: March 20, 2026, 1:45 p.m.