Triple
T20877056
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stephen J. Cannell |
E514046
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Baretta |
—
|
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: Baretta | Statement: [Stephen J. Cannell, notableWork, Baretta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Baretta Context triple: [Stephen J. Cannell, notableWork, Baretta]
-
A.
Baretta
chosen
Baretta is a 1970s American television crime drama series centered on an unconventional undercover police detective.
-
B.
Pakize
Pakize is a fictional character in Ahmet Hamdi Tanpınar’s novel "Saatleri Ayarlama Enstitüsü," known as one of the women connected to the protagonist Hayri İrdal.
-
C.
Luella Gear
Luella Gear was an American actress and comedian known for her work in early 20th-century stage and film productions.
-
D.
Agent 86
Agent 86 is the bumbling yet resourceful secret agent protagonist of the classic satirical spy television series "Get Smart."
-
E.
Fusco
Fusco is a character in the crime drama film "Dinner Rush," involved in the tense, interwoven events surrounding a New York City restaurant.
- 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.