Triple

T8391725
Position Surface form Disambiguated ID Type / Status
Subject Melissa Rauch E197957 entity
Predicate hasRole P161 FINISHED
Object Marie
Marie is a character portrayed by actress and comedian Melissa Rauch, known for her work on television and in film.
 E27948 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: Marie | Statement: [Melissa Rauch, hasRole, Marie]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Marie
Context triple: [Melissa Rauch, hasRole, Marie]
  • A. Marie
    Marie is a widely used European given name, especially common in French-speaking countries, derived from the Hebrew name Miryam (Mary).
  • B. Marie Christine
    Marie Christine, better known as Princess Michael of Kent, is a member of the British royal family, an author, and the wife of Prince Michael of Kent, a first cousin of King Charles III.
  • C. Marie Émilie
    Marie Émilie is a French noblewoman best known as the morganatic wife of Louis, Grand Dauphin of France, during the late 17th and early 18th centuries.
  • D. 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.
  • E. Renée
    Renée is a feminine given name of French origin, commonly used in French-speaking countries and beyond.
  • 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: Marie
Triple: [Melissa Rauch, hasRole, Marie]
Generated description
Marie is a character portrayed by actress and comedian Melissa Rauch, known for her work on television and in film.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Marie
Target entity description: Marie is a character portrayed by actress and comedian Melissa Rauch, known for her work on television and in film.
  • A. Marie chosen
    Marie is a widely used European given name, especially common in French-speaking countries, derived from the Hebrew name Miryam (Mary).
  • B. Marie Christine
    Marie Christine, better known as Princess Michael of Kent, is a member of the British royal family, an author, and the wife of Prince Michael of Kent, a first cousin of King Charles III.
  • C. Marie Émilie
    Marie Émilie is a French noblewoman best known as the morganatic wife of Louis, Grand Dauphin of France, during the late 17th and early 18th centuries.
  • D. 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.
  • E. Renée
    Renée is a feminine given name of French origin, commonly used in French-speaking countries and beyond.
  • 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_69ca82f749388190bffbea6dfb509016 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb810e16b081908e2c25bfb9d590ed completed March 31, 2026, 8:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69cde84d6f3c8190ba12905ba5900087 completed April 2, 2026, 3:53 a.m.
NEDg Description generation batch_69cdebfc63e8819087f5c1d588b58e21 completed April 2, 2026, 4:09 a.m.
NED2 Entity disambiguation (via description) batch_69cded77618c81909e8786ccd2f3e4b6 completed April 2, 2026, 4:15 a.m.
Created at: March 30, 2026, 6:03 p.m.