Triple
T7603938
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Lion King (2019 film) |
E180053
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Jeffrey Silver |
E149699
|
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: Jeffrey Silver | Statement: [The Lion King (2019 film), producer, Jeffrey Silver]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeffrey Silver Context triple: [The Lion King (2019 film), producer, Jeffrey Silver]
-
A.
Jeffrey Silver
chosen
Jeffrey Silver is a film producer known for his work on major Hollywood movies, including the science fiction action film "Terminator Salvation."
-
B.
Stephen Rubin
Stephen Rubin is a film producer best known for his work on the drama film "The Deep End of the Ocean."
-
C.
Michael Greenberg
Michael Greenberg is a prominent American neuroscientist renowned for his pioneering work on activity-dependent gene expression in the brain.
-
D.
Dan Levine
Dan Levine is a film producer best known for his work on the acclaimed science-fiction drama "Arrival."
-
E.
Greg Medavoy
Greg Medavoy is a mild-mannered, often self-doubting detective whose personal growth and quiet competence provide both comic relief and emotional depth throughout the TV series "NYPD Blue."
- 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_69c69f3567008190ab01d2ca7b53584a |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f9fbd1408190b721bf016f997c7b |
completed | March 27, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8684f3cf08190bc3cbafbd8c1b9a0 |
completed | March 28, 2026, 11:46 p.m. |
Created at: March 27, 2026, 3:54 p.m.