Triple
T21974972
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Outpost |
E542680
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Jonathan Yunger |
—
|
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 Yunger | Statement: [The Outpost, producer, Jonathan Yunger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jonathan Yunger Context triple: [The Outpost, producer, Jonathan Yunger]
-
A.
Jonathan Yunger
chosen
Jonathan Yunger is a film producer known for his work on action and war dramas, including the military thriller "The Outpost."
-
B.
Jonathan Benassaya
Jonathan Benassaya is a French entrepreneur best known for co-founding the music streaming service Deezer.
-
C.
Joshua Rudoy
Joshua Rudoy is an American former child actor best known for his role in the 1987 family comedy film "Harry and the Hendersons."
-
D.
Eli Nunn
Eli Nunn is the child of English actress and writer Imogen Stubbs.
-
E.
Jared Kleinman
Jared Kleinman is a sarcastic, tech-savvy high school student who serves as comic relief and a reluctant accomplice in the musical "Dear Evan Hansen."
- 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_69e0c48070988190909db97667b9a0ac |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f12487a1a88190abb8a51fcd533b6a |
completed | April 28, 2026, 9:20 p.m. |
Created at: April 16, 2026, 8:03 p.m.