Triple
T20265123
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red |
E498946
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Jon Hoeber |
—
|
NE NERFINISHED |
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: Jon Hoeber | Statement: [Red, screenwriter, Jon Hoeber]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jon Hoeber Context triple: [Red, screenwriter, Jon Hoeber]
-
A.
Jon Hoeber
chosen
Jon Hoeber is an American screenwriter best known for co-writing action and thriller films such as "Red" and its sequel, as well as various big-budget genre movies.
-
B.
Ken Hofmann
Ken Hofmann was an American real estate developer and sports team owner best known for co-owning Major League Baseball’s Oakland Athletics.
-
C.
John Diehl
John Diehl is an American character actor best known for his role as Detective Larry Zito on the 1980s television series "Miami Vice."
-
D.
John Eisendrath
John Eisendrath is a television writer and producer best known for his work on series such as "The Blacklist" and "Alias."
-
E.
William Haade
William Haade was an American character actor known for his tough-guy roles in numerous Hollywood films from the 1930s through the 1950s.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da6275fa6c8190952924930adee150 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e674ce27688190b31d7a6c98d3ec5e |
completed | April 20, 2026, 6:47 p.m. |
Created at: April 11, 2026, 11:41 p.m.