Triple
T31415795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North American film market |
E801389
|
entity |
| Predicate | dominantCountry |
P48279
|
FINISHED |
| Object | United States |
—
|
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: United States | Statement: [North American film market, dominantCountry, United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dominantCountry Context triple: [North American film market, dominantCountry, United States]
-
A.
primaryLeaderCountry
Indicates that a person is the main political leader (e.g., head of state or government) of a specified country.
-
B.
countryDominating
Indicates that one country holds a position of control, dominance, or decisive influence over another country.
-
C.
dominantMediaMarket
Indicates that one media market holds primary influence or control over another media market in terms of audience reach or content distribution.
-
D.
mainCountry
chosen
Indicates that one country is the primary or most significant country associated with a given entity or context.
-
E.
capitalCountry
Indicates that one place serves as the capital city of a given country.
- F. None of above.
Provenance (3 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_69f348c0dd648190bf2fd7642f78eb06 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f7431c0eec81909ead443e07d75e18 |
completed | May 3, 2026, 12:44 p.m. |
| PD | Predicate disambiguation | batch_69f74143cf708190a12d487884298437 |
completed | May 3, 2026, 12:36 p.m. |
Created at: April 30, 2026, 8:43 p.m.