Triple

T1769047
Position Surface form Disambiguated ID Type / Status
Subject Magdalena E38830 entity
Predicate hasVariant P455 FINISHED
Object Madeleine
Madeleine is a feminine given name, commonly used in French and English, derived from Magdalene and often associated with literary and cultural figures.
 E215457 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: Madeleine | Statement: [Magdalena, hasVariant, Madeleine]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Madeleine
Context triple: [Magdalena, hasVariant, Madeleine]
  • 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. Marie
    Marie is a widely used European given name, especially common in French-speaking countries, derived from the Hebrew name Miryam (Mary).
  • C. Renée
    Renée is a feminine given name of French origin, commonly used in French-speaking countries and beyond.
  • 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. 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: Madeleine
Triple: [Magdalena, hasVariant, Madeleine]
Generated description
Madeleine is a feminine given name, commonly used in French and English, derived from Magdalene and often associated with literary and cultural figures.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Madeleine
Target entity description: Madeleine is a feminine given name, commonly used in French and English, derived from Magdalene and often associated with literary and cultural figures.
  • 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. Marie
    Marie is a widely used European given name, especially common in French-speaking countries, derived from the Hebrew name Miryam (Mary).
  • C. Renée
    Renée is a feminine given name of French origin, commonly used in French-speaking countries and beyond.
  • D. Antoinette
    Antoinette is the birth name of Princess Muna al-Hussein, the British-born mother of King Abdullah II of Jordan.
  • 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_69a8862e61708190af97b9838cc3f5de completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa648d9f2c8190aca4884648a69eb0 completed March 6, 2026, 5:22 a.m.
NED1 Entity disambiguation (via context triple) batch_69adf3bc8fe8819085a8adaf9dcd5c1b completed March 8, 2026, 10:10 p.m.
NEDg Description generation batch_69adf494f0288190bf77285d18fcd3d9 completed March 8, 2026, 10:13 p.m.
NED2 Entity disambiguation (via description) batch_69adf51f49488190b9b0465b4da685c1 completed March 8, 2026, 10:15 p.m.
Created at: March 4, 2026, 7:31 p.m.