Triple
T22126090
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I Am |
E546793
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Jon Levine |
—
|
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 Levine | Statement: [I Am, producer, Jon Levine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jon Levine Context triple: [I Am, producer, Jon Levine]
-
A.
Jon Levine
chosen
Jon Levine is a Canadian music producer, songwriter, and musician known for his work with various pop and R&B artists.
-
B.
Jonathan Levine
Jonathan Levine is an American film director and screenwriter known for character-driven comedies and dramedies such as 50/50 and Warm Bodies.
-
C.
Jesse Harlin
Jesse Harlin is a video game music composer known for his work on major titles including The Sims 4.
-
D.
Jonathan A. Levine
Jonathan A. Levine is an American film director and screenwriter known for movies such as "50/50," "Warm Bodies," and "The Wackness."
-
E.
Jeremy Leven
Jeremy Leven is an American screenwriter, director, and novelist known for adapting romantic and character-driven stories for film, including the hit movie "The Notebook."
- 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_69e11e39bf348190b541bfa16a7b71e0 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f12981ac008190aafb516f2a28fefc |
completed | April 28, 2026, 9:41 p.m. |
Created at: April 16, 2026, 8:31 p.m.