Triple
T18253016
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cash Truck |
E437145
|
entity |
| Predicate | workOfArtType |
P116586
|
FINISHED |
| Object | French 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: French cinema | Statement: [Cash Truck, workOfArtType, French cinema]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: workOfArtType Context triple: [Cash Truck, workOfArtType, French cinema]
-
A.
artworkType
Indicates the specific category or kind of artwork that characterizes the relationship between the subject and the artwork.
-
B.
artCategory
chosen
Indicates the classification relationship where an artwork is assigned to a particular artistic category or genre.
-
C.
artSpecialty
Indicates that an entity’s primary focus, expertise, or specialization is in a particular art form or artistic domain.
-
D.
artisticMedium
Indicates the material or technique used to create an artwork or artistic expression.
-
E.
exhibitionType
Indicates the specific category or kind of exhibition associated with an entity (e.g., art show, trade fair, scientific exhibit).
- 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_69d8b913351c8190932b6a426de04b41 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4fd81ea3481909d96b5399f7a32b3 |
completed | April 19, 2026, 4:06 p.m. |
| PD | Predicate disambiguation | batch_69e44fcdee748190bae6fb76e0cb22f3 |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:33 a.m.