Triple
T9205098
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mark Gruner |
E220954
|
entity |
| Predicate | portrayed |
P1668
|
FINISHED |
| Object | Mike Brody |
E709787
|
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: Mike Brody | Statement: [Mark Gruner, portrayed, Mike Brody]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mike Brody Context triple: [Mark Gruner, portrayed, Mike Brody]
-
A.
Mike Brody
chosen
Mike Brody is a recurring character in the Jaws film series, known as the son of police chief Martin Brody who continues to confront deadly shark threats as an adult.
-
B.
Sean Brody
Sean Brody is a fictional character from the "Jaws" film series, known as the younger son of police chief Martin Brody.
-
C.
Dave Trager
Dave Trager was a sports executive best known for owning the early NBA franchise that became the Chicago Packers.
-
D.
Mike Nolan
Mike Nolan is a name shared by several notable individuals, including a British singer from the pop group Bucks Fizz and various sports coaches and players.
-
E.
Ted Briskin
Ted Briskin was an American businessman best known as the first husband of Hollywood actress and singer Betty Hutton.
- 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_69ccd945f37881909f0d30eeb6a7a3ad |
completed | April 1, 2026, 8:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d05c4e56208190a5b2749b81e467be |
completed | April 4, 2026, 12:33 a.m. |
Created at: March 30, 2026, 7:26 p.m.