Triple
T4536825
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | George (magazine) |
E107425
|
entity |
| Predicate | coFounder |
P2835
|
FINISHED |
| Object | Michael Berman |
E525446
|
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: Michael Berman | Statement: [George (magazine), coFounder, Michael Berman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Berman Context triple: [George (magazine), coFounder, Michael Berman]
-
A.
Michael Berman
chosen
Michael Berman is a writer and contributor known for his work published in George magazine.
-
B.
Steven Baigelman
Steven Baigelman is an American screenwriter and producer known for his work on biographical and crime dramas in film and television.
-
C.
Mitch Kertzman
Mitch Kertzman is an American technology executive and entrepreneur best known for his leadership roles in the software and semiconductor industries, including at companies like LSI Logic and Sybase.
-
D.
Pandro S. Berman
Pandro S. Berman was a prominent American film producer of Hollywood’s classic era, known for overseeing numerous successful MGM and RKO pictures.
-
E.
Michael Greenberg
Michael Greenberg is a prominent American neuroscientist renowned for his pioneering work on activity-dependent gene expression in the brain.
- 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_69bd43f922788190b7edfa294e39b178 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd57b78b8481909d79131723d4be22 |
completed | March 20, 2026, 2:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf91e444b081909d97eebf04d7f380 |
completed | March 22, 2026, 6:53 a.m. |
Created at: March 20, 2026, 1:04 p.m.