Triple
T18835371
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monet Mazur |
E460649
|
entity |
| Predicate | relative |
P37
|
FINISHED |
| Object |
Matisse Mazur
Matisse Mazur is the son of American actress and model Monet Mazur.
|
E1345474
|
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: Matisse Mazur | Statement: [Monet Mazur, relative, Matisse Mazur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matisse Mazur Context triple: [Monet Mazur, relative, Matisse Mazur]
-
A.
Cezanne Mazur
Cezanne Mazur is a member of the Mazur family, known primarily for being related to American actress and model Monet Mazur.
-
B.
Monet Mazur
Monet Mazur is an American actress and model best known for her film and television roles, including a lead role on the sports drama series "All American."
-
C.
Horace Manges
Horace Manges was an American lawyer and co-founder of the prominent international law firm Weil, Gotshal & Manges LLP.
-
D.
Liv Corfixen
Liv Corfixen is a Danish actress and documentary filmmaker known for her work on projects related to director Nicolas Winding Refn.
-
E.
Marcel Bleustein-Blanchet
Marcel Bleustein-Blanchet was a pioneering French advertising executive and entrepreneur who became one of the key figures in the development of modern advertising in France.
- 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: Matisse Mazur Triple: [Monet Mazur, relative, Matisse Mazur]
Generated description
Matisse Mazur is the son of American actress and model Monet Mazur.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Matisse Mazur Target entity description: Matisse Mazur is the son of American actress and model Monet Mazur.
-
A.
Cezanne Mazur
chosen
Cezanne Mazur is a member of the Mazur family, known primarily for being related to American actress and model Monet Mazur.
-
B.
Monet Mazur
Monet Mazur is an American actress and model best known for her film and television roles, including a lead role on the sports drama series "All American."
-
C.
Horace Manges
Horace Manges was an American lawyer and co-founder of the prominent international law firm Weil, Gotshal & Manges LLP.
-
D.
Liv Corfixen
Liv Corfixen is a Danish actress and documentary filmmaker known for her work on projects related to director Nicolas Winding Refn.
-
E.
Marcel Bleustein-Blanchet
Marcel Bleustein-Blanchet was a pioneering French advertising executive and entrepreneur who became one of the key figures in the development of modern advertising in France.
- 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_69d8dcfa11e4819090ab1ef5bdcd2b2e |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5a99d491c81909d8e55ac45621d44 |
completed | April 20, 2026, 4:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0575ade5988190aa12b36e5ae45fc0 |
completed | May 14, 2026, 7:11 a.m. |
| NEDg | Description generation | batch_6a05763116908190b65f8c86655d9496 |
completed | May 14, 2026, 7:13 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a057a19f3b88190a6b9dc4c20b39295 |
completed | May 14, 2026, 7:30 a.m. |
Created at: April 10, 2026, 11:56 a.m.