Triple
T21463191
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tony Garnett |
E529525
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | World Productions |
—
|
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: World Productions | Statement: [Tony Garnett, employer, World Productions]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: World Productions Context triple: [Tony Garnett, employer, World Productions]
-
A.
World Productions
chosen
World Productions is a British television production company known for creating acclaimed drama series such as Line of Duty and Bodyguard.
-
B.
Beyond Productions
Beyond Productions is an Australian television production company best known for creating and producing the popular science-entertainment series MythBusters.
-
C.
Company Productions
Company Productions is a film production company best known for producing the acclaimed Tamil movie "Subramaniapuram."
-
D.
Relative Productions
Relative Productions is a film production company best known for its work on the comedy movie "Mr. Woodcock."
-
E.
Pacific Productions
Pacific Productions is a television production company best known for producing the popular cooking and lifestyle series "The Pioneer Woman."
- 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_69e0c458133481908ae8b41a12c4edec |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9e9efdb188190be79b72e1bd18860 |
completed | April 23, 2026, 9:44 a.m. |
Created at: April 16, 2026, 6:09 p.m.