Triple
T1302551
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ordre des Arts et des Lettres |
E27798
|
entity |
| Predicate | notableRecipientField |
P14626
|
FINISHED |
| Object | cinema |
—
|
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: cinema | Statement: [Ordre des Arts et des Lettres, notableRecipientField, cinema]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableRecipientField Context triple: [Ordre des Arts et des Lettres, notableRecipientField, cinema]
-
A.
notableRecipient
Indicates that an entity has received a notable award, honor, or recognition from another entity.
-
B.
notableRecipientType
Indicates that an entity is notably recognized as a recipient of something (such as an award, honor, or distinction) of a specified type.
-
C.
notableFieldOfRecipients
chosen
Indicates that the recipients are notable or recognized specifically in a particular field or area of expertise.
-
D.
individualRecipient
Indicates that a specific individual is the direct recipient or beneficiary of something (such as an item, message, or action).
-
E.
primaryRecipient
Indicates the entity that is the main or principal receiver of something, such as a message, resource, or benefit, in a given context.
- 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_69a496d7d83481908f83085854e51328 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c115ba64819081c55fa6807e19ef |
completed | March 1, 2026, 10:43 p.m. |
| PD | Predicate disambiguation | batch_69a4bee8544c8190874efd9bae9bccf9 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:51 p.m.