Triple
T15063588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monster-in-Law |
E379698
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Monet Mazur |
E460649
|
NE FINISHED |
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: [Monster-in-Law, starring, Monet Mazur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monet Mazur Context triple: [Monster-in-Law, starring, 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.
Marya Mannes
Marya Mannes was an American writer, critic, and social commentator known for her sharp wit and incisive observations on mid-20th-century culture and politics.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d85cd7683881908d405c1b5d7b4f7f |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69dedee803ac81908bb7d66e49c2eb72 |
completed | April 15, 2026, 12:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fea5c8b3ac8190b8fc921b6e6eeed5 |
completed | May 9, 2026, 3:11 a.m. |
Created at: April 10, 2026, 3:02 a.m.