Triple
T9134871
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Skechers |
E219174
|
entity |
| Predicate | hasKeyPerson |
P256
|
FINISHED |
| Object | Michael Greenberg |
E819521
|
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 Greenberg | Statement: [Skechers, hasKeyPerson, Michael Greenberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Greenberg Context triple: [Skechers, hasKeyPerson, Michael Greenberg]
-
A.
Michael Greenberg
Michael Greenberg is a prominent American neuroscientist renowned for his pioneering work on activity-dependent gene expression in the brain.
-
B.
Michael Greenberg
chosen
Michael Greenberg is an American businessman best known as the co-founder and longtime executive leader of the global footwear company Skechers.
-
C.
Mike Greenberg
Mike Greenberg is an American television and radio sportscaster best known as a longtime ESPN personality and co-host of popular sports talk shows.
-
D.
Steve Greenberg
Steve Greenberg is an American sports media executive and entrepreneur best known for founding the Classic Sports Network, which later became ESPN Classic.
-
E.
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.
- 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_69ca83e012288190a5771058adbaabd2 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca8de0dec8190978c80b9ec8bf25c |
completed | April 1, 2026, 5:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1c3f31db48190a63d0d60f108496f |
completed | April 5, 2026, 2:07 a.m. |
Created at: March 30, 2026, 7:18 p.m.