Triple
T20877112
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stephen J. Cannell Productions |
E514047
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Booker |
—
|
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: Booker | Statement: [Stephen J. Cannell Productions, notableWork, Booker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Booker Context triple: [Stephen J. Cannell Productions, notableWork, Booker]
-
A.
Booker
Booker is a character associated with Rain, likely appearing in the same narrative or fictional universe.
-
B.
Booker
Booker is the given name of Booker T. Washington, the influential African American educator, author, and leader in the late 19th and early 20th centuries.
-
C.
Booker
Booker is a surname most prominently associated with Cory Booker, a U.S. Senator from New Jersey and former mayor of Newark.
-
D.
Booker
chosen
"Booker" is an American television series best known as a spin-off of the hit show "21 Jump Street," following the character Dennis Booker as he works as an investigator for a corporate security firm.
-
E.
Booker
Booker is a weary, immortal warrior and member of the covert team in "The Old Guard," known for his tragic backstory and conflicted loyalty.
- 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_69e0b4f733f081908a401c0b7beb0b9f |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c6767ec0819080721e2e75bd0d66 |
completed | April 21, 2026, 12:36 a.m. |
Created at: April 16, 2026, 12:45 p.m.