Triple

T14100006
Position Surface form Disambiguated ID Type / Status
Subject Anne Marie Martinozzi E339353 entity
Predicate givenName P17 FINISHED
Object Anne Marie
Anne Marie is a feminine given name of French origin commonly used in various European and English-speaking countries.
 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 Martinozzi, givenName, Anne Marie]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Anne Marie
Context triple: [Anne Marie Martinozzi, givenName, Anne Marie]
  • A. 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.
  • B. 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.
  • C. Mary Marie
    Mary Marie is a novel by Eleanor H. Porter, best known as the author of "Pollyanna," and features a young girl navigating the emotional upheaval of her parents’ divorce.
  • D. Maryanne
    Maryanne is a feminine given name, often used in English-speaking countries as a variant of Mary Ann or Marianne.
  • E. 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.
  • 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 Martinozzi, givenName, Anne Marie]
Generated description
Anne Marie is a feminine given name of French origin commonly used in various European and English-speaking countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Anne Marie
Target entity description: Anne Marie is a feminine given name of French origin commonly used in various European and English-speaking countries.
  • A. Anne Marie chosen
    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.
  • B. 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.
  • C. Mary Marie
    Mary Marie is a novel by Eleanor H. Porter, best known as the author of "Pollyanna," and features a young girl navigating the emotional upheaval of her parents’ divorce.
  • D. Maryanne
    Maryanne is a feminine given name, often used in English-speaking countries as a variant of Mary Ann or Marianne.
  • E. 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.
  • F. None of above.

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_69d81c69b5c8819094aa1abf18302908 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de5fba7c10819095b1299b7b4f0310 completed April 14, 2026, 3:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcd0b108908190b4b408f21ecb877a completed May 7, 2026, 5:49 p.m.
NEDg Description generation batch_69fcd5533dc88190b0ca6c0d7d47d84e completed May 7, 2026, 6:09 p.m.
NED2 Entity disambiguation (via description) batch_69fcd61f06e881909c3c42b83f858471 completed May 7, 2026, 6:12 p.m.
Created at: April 9, 2026, 10:22 p.m.