Triple
T19095506
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blue Bayou |
E467393
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Kim Roth |
—
|
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: Kim Roth | Statement: [Blue Bayou, producer, Kim Roth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kim Roth Context triple: [Blue Bayou, producer, Kim Roth]
-
A.
Kim Roth
chosen
Kim Roth is a film producer best known for her work on critically acclaimed dramas such as "Mudbound."
-
B.
Tamara Diane Miller
Tamara Diane Miller is a member of the extended Disney family, descended from the lineage of Walt Disney’s relatives.
-
C.
Melissa Hensarling
Melissa Hensarling is the wife of former U.S. Congressman Jeb Hensarling and a figure known primarily in connection with his political career.
-
D.
Ken Loeffler
Ken Loeffler was an American basketball coach best known for leading La Salle University to national prominence in the 1950s and for his successful stints in both college and professional basketball.
-
E.
Kay Bailey Hutchison
Kay Bailey Hutchison is an American attorney and Republican politician who served as a long-time U.S. Senator from Texas.
- 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_69d8dd05ac4c8190b1967d8f97f3fb2f |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e368f20c8190bd84d2ba320991ac |
completed | April 20, 2026, 8:27 a.m. |
Created at: April 10, 2026, 12:04 p.m.