Triple
T9205360
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Cohen |
E220961
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Laura Shusterman |
E220961
|
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: Laura Shusterman | Statement: [Michael Cohen, spouse, Laura Shusterman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laura Shusterman Context triple: [Michael Cohen, spouse, Laura Shusterman]
-
A.
Laura Shusterman
chosen
Laura Shusterman is the wife of former Donald Trump attorney Michael Cohen and a Ukrainian-born businesswoman who has been linked to some of his real estate and taxi-medallion ventures.
-
B.
Rachel Leibowitz
Rachel Leibowitz is a person notable enough to be specifically cited as a bearer of the surname Leibowitz.
-
C.
Ellen Mirojnick
Ellen Mirojnick is an American costume designer known for her influential work on films such as "Basic Instinct" and numerous other high-profile productions.
-
D.
Rachel Buchman
Rachel Buchman is the titular bride and central figure in the 2008 drama film "Rachel Getting Married," around whose wedding and family tensions the story revolves.
-
E.
Deborah Liebling
Deborah Liebling is an American television and film producer and executive known for her work on comedy projects.
- 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_69ca83e8e9248190862cf3e41693b310 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccd947a0a08190966f22a6207c9120 |
completed | April 1, 2026, 8:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d19f50f1c4819099a9c511f58e9873 |
completed | April 4, 2026, 11:31 p.m. |
Created at: March 30, 2026, 7:26 p.m.