Triple
T18337499
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Just Married |
E439309
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Monet Mazur |
—
|
NE NERFINISHED |
How this triple was built (2 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: Monet Mazur | Statement: [Just Married, castMember, Monet Mazur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monet Mazur Context triple: [Just Married, castMember, Monet Mazur]
-
A.
Monet Mazur
chosen
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."
-
B.
Paula Mazur
Paula Mazur is a film producer best known for adapting literary works, including the screen version of "The Guernsey Literary and Potato Peel Pie Society."
-
C.
Juliana Minsky
Juliana Minsky is a daughter of pioneering artificial intelligence researcher Marvin Minsky.
-
D.
Juliana Minsky
Juliana Minsky is a member of the Minsky family, related to Henry Minsky and connected to the legacy of computer scientist Marvin Minsky.
-
E.
Tania Ghirshman
Tania Ghirshman was a French archaeologist and art collector known for her work alongside her husband Roman Ghirshman on excavations and the study of ancient Near Eastern art.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9175fec8190af865699b4e64d8c |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e50ece387881909a5da0aa4370489c |
completed | April 19, 2026, 5:20 p.m. |
Created at: April 10, 2026, 10:37 a.m.