Triple
T13011332
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Magritte Award for Best Foreign Film in Coproduction |
E322417
|
entity |
| Predicate | hasWinners |
P107819
|
FINISHED |
| Object | foreign coproduced films |
—
|
LITERAL FINISHED |
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: foreign coproduced films | Statement: [Magritte Award for Best Foreign Film in Coproduction, hasWinners, foreign coproduced films]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWinners Context triple: [Magritte Award for Best Foreign Film in Coproduction, hasWinners, foreign coproduced films]
-
A.
hasMultipleWinnersPossible
Indicates that a situation, event, or contest allows for more than one winner to be recognized or selected.
-
B.
hasNumberOfFinalists
Indicates the relationship between an entity and the count of finalists associated with it.
-
C.
hasWinnerType
Indicates that an entity has a specific type or category of winner associated with it.
-
D.
hasAwarded
Indicates that one entity has given or conferred an award to another entity.
-
E.
hasNotableRegionOfWinners
Indicates that an entity is associated with a specific geographic region characterized by a notable concentration or pattern of winners.
- F. None of above. chosen
Provenance (4 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_69d807657e8c8190bd9435ee2f823845 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97e9e14b88190a2cee8e0c9bf31c8 |
completed | April 10, 2026, 10:50 p.m. |
| PD | Predicate disambiguation | batch_69d97dc153a081909d13a694993f074a |
completed | April 10, 2026, 10:46 p.m. |
| PDg | Predicate description generation | batch_69d97e74283c819082e69ac3554fa7d8 |
completed | April 10, 2026, 10:49 p.m. |
Created at: April 9, 2026, 8:49 p.m.