Triple
T19771722
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red State |
E474903
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Jonathan Gordon |
—
|
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: Jonathan Gordon | Statement: [Red State, producer, Jonathan Gordon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jonathan Gordon Context triple: [Red State, producer, Jonathan Gordon]
-
A.
Jonathan Gordon
chosen
Jonathan Gordon is a film producer best known for his work on acclaimed movies such as "Silver Linings Playbook."
-
B.
Christopher Gordon
Christopher Gordon is an Australian composer best known for his orchestral film scores, including his acclaimed work on "Master and Commander: The Far Side of the World."
-
C.
Len Goldstein
Len Goldstein is a television producer known for his executive production work on series such as "The Astronaut Wives Club."
-
D.
Jeffrey Goodman
Jeffrey Goodman is an entrepreneur best known as a founder of the online auto insurance company Esurance.
-
E.
Eric L. Gold
Eric L. Gold is a film and television producer best known for his work on the horror-comedy franchise "Scary Movie."
- 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_69d8e51a43a08190956bc6df13c91a77 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6535ce4d08190a1dfca2df95a8631 |
completed | April 20, 2026, 4:25 p.m. |
Created at: April 10, 2026, 1:48 p.m.