Triple
T7700641
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | César Award for Best Writing |
E174483
|
entity |
| Predicate | hasAwardedWork |
P8623
|
FINISHED |
| Object | French feature 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: French feature films | Statement: [César Award for Best Writing, hasAwardedWork, French feature films]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAwardedWork Context triple: [César Award for Best Writing, hasAwardedWork, French feature films]
-
A.
hasAwardedWorkType
chosen
Indicates that an entity has granted or is associated with a specific type or category of work that has received an award.
-
B.
hasAwarded
Indicates that one entity has given or conferred an award to another entity.
-
C.
hasAwardedFieldOfWork
Indicates that an entity has received an award specifically for work or contributions in a particular field or area of activity.
-
D.
hasAwardConnection
Indicates a relationship where an entity is connected to an award, such as by receiving, being nominated for, or otherwise associated with that award.
-
E.
hasWorkAttributed
Indicates that a particular work or creation is credited or assigned as being produced by a specific agent or entity.
- 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_69c6995a72cc8190998e56daa6f8e453 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c70402169481909b219dc5f4a64b9b |
completed | March 27, 2026, 10:26 p.m. |
| PD | Predicate disambiguation | batch_69c70165e78c8190bf6b3c34e243cb81 |
completed | March 27, 2026, 10:15 p.m. |
Created at: March 27, 2026, 4:03 p.m.