Triple
T13602903
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meet Joe Black |
E324987
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Claire Rudnick Polstein |
E515892
|
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: Claire Rudnick Polstein | Statement: [Meet Joe Black, producer, Claire Rudnick Polstein]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Claire Rudnick Polstein Context triple: [Meet Joe Black, producer, Claire Rudnick Polstein]
-
A.
Claire Rudnick Polstein
chosen
Claire Rudnick Polstein is a film producer best known for her work on the drama feature "The Company Men."
-
B.
Rachel Leibowitz
Rachel Leibowitz is a person notable enough to be specifically cited as a bearer of the surname Leibowitz.
-
C.
Jessica Gelman
Jessica Gelman is a prominent sports analytics executive and entrepreneur best known for co-founding and leading the influential MIT Sloan Sports Analytics Conference.
-
D.
Janet Margolin
Janet Margolin was an American film and television actress best known for her roles in movies such as "David and Lisa" and Woody Allen's "Annie Hall."
-
E.
Claudia Finkelstein
Claudia Finkelstein is a physician and academic known for her work in internal medicine and physician well-being.
- 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_69d80769eaf081909d82f44e484d6113 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb07ca07481909c45da551ea61ab4 |
completed | April 12, 2026, 2:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd8a9c41908190b789765861bd9924 |
completed | May 8, 2026, 7:02 a.m. |
Created at: April 9, 2026, 9:49 p.m.