Triple
T18174247
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Future |
E435112
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Leanne Ungar |
—
|
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: Leanne Ungar | Statement: [The Future, producer, Leanne Ungar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leanne Ungar Context triple: [The Future, producer, Leanne Ungar]
-
A.
Leanne Ungar
chosen
Leanne Ungar is a Grammy-winning American record producer and audio engineer best known for her long-term collaboration with Leonard Cohen and work on numerous acclaimed folk and rock albums.
-
B.
Leigh-Anne Csuhany
Leigh-Anne Csuhany is best known as the former wife of American actor and comedian Kelsey Grammer.
-
C.
Sharona Fleming
Sharona Fleming is a fictional nurse and assertive personal assistant to detective Adrian Monk on the television series "Monk."
-
D.
Angela Reide
Angela Reide is the central protagonist of "The Division," around whom the story’s key events and character dynamics revolve.
-
E.
Cheri Steinkellner
Cheri Steinkellner is an American television writer and producer best known for her work on acclaimed sitcoms such as "Cheers" and "Bob," often collaborating with her husband Bill Steinkellner.
- 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_69d8b90b7a188190b3fc7b8d4a6cd20a |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4df5900788190994525954a89fa3c |
completed | April 19, 2026, 1:57 p.m. |
Created at: April 10, 2026, 10:30 a.m.