Triple

T6924604
Position Surface form Disambiguated ID Type / Status
Subject Anne Marie d’Orléans E160273 entity
Predicate givenName P17 FINISHED
Object Anne Marie
Anne Marie was a French princess of the House of Orléans who became Queen of Sardinia through her marriage to Victor Amadeus II of Savoy.
 E630163 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: Anne Marie | Statement: [Anne Marie d’Orléans, givenName, Anne Marie]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Anne Marie
Context triple: [Anne Marie d’Orléans, givenName, Anne Marie]
  • A. Anna Marie
    Anna Marie, better known as Rogue, is a popular Marvel Comics superhero and longtime member of the X-Men who absorbs others’ powers and memories through touch.
  • B. Maryanne
    Maryanne is a feminine given name, often used in English-speaking countries as a variant of Mary Ann or Marianne.
  • C. Anna
    Anna is the tragic, aristocratic heroine of Leo Tolstoy’s novel "Anna Karenina," whose passionate affair and struggle against societal norms lead to her downfall.
  • D. Anna
    Anna is the given name of Anna Murray Douglass, an African American abolitionist and the first wife of Frederick Douglass.
  • E. Anna
    Anna is the given name of pioneering Chinese American actress Anna May Wong, a trailblazing early Hollywood star and fashion icon.
  • 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: Anne Marie
Triple: [Anne Marie d’Orléans, givenName, Anne Marie]
Generated description
Anne Marie was a French princess of the House of Orléans who became Queen of Sardinia through her marriage to Victor Amadeus II of Savoy.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Anne Marie
Target entity description: Anne Marie was a French princess of the House of Orléans who became Queen of Sardinia through her marriage to Victor Amadeus II of Savoy.
  • A. Anna Marie
    Anna Marie, better known as Rogue, is a popular Marvel Comics superhero and longtime member of the X-Men who absorbs others’ powers and memories through touch.
  • B. Maryanne
    Maryanne is a feminine given name, often used in English-speaking countries as a variant of Mary Ann or Marianne.
  • C. Anna
    Anna is the tragic, aristocratic heroine of Leo Tolstoy’s novel "Anna Karenina," whose passionate affair and struggle against societal norms lead to her downfall.
  • D. Anna
    Anna is the given name of pioneering Chinese American actress Anna May Wong, a trailblazing early Hollywood star and fashion icon.
  • E. Anna
    Anna is a character from the video game "Surfacing," likely serving as a key figure in the game's narrative or player interactions.
  • 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_69c6884d350081908d8a970e4d40ad78 completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6da18b6388190947dfc1eb9e5d382 completed March 27, 2026, 7:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7513bcd2c8190853bc6e8a33a1673 completed March 28, 2026, 3:55 a.m.
NEDg Description generation batch_69c751abed548190ac2152acd3029d2d completed March 28, 2026, 3:57 a.m.
NED2 Entity disambiguation (via description) batch_69c7558dd72081909af14d319ce01ff6 completed March 28, 2026, 4:14 a.m.
Created at: March 27, 2026, 2:26 p.m.