Triple
T1802826
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tamil cinema |
E39755
|
entity |
| Predicate | hasTechnologyAdoption |
P1485
|
FINISHED |
| Object | digital cinematography |
—
|
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: digital cinematography | Statement: [Tamil cinema, hasTechnologyAdoption, digital cinematography]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTechnologyAdoption Context triple: [Tamil cinema, hasTechnologyAdoption, digital cinematography]
-
A.
marketAdoption
Indicates the extent to which a product, service, or innovation has been accepted and used by its target market or customer base.
-
B.
laterUsedTechnology
Indicates that one entity adopted or employed a technology after another entity had already used it.
-
C.
associatedWithTechnology
chosen
Indicates a relationship where an entity is connected to, involved with, or utilizes a particular technology.
-
D.
hadStrongerAdoptionIn
Indicates that one entity was adopted, accepted, or taken up more extensively or rapidly within a specified context, group, or region than another.
-
E.
hasAdvancedTechnologySector
Indicates that an entity possesses or includes a developed sector focused on advanced or high-tech industries, products, or services.
- 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_69a88632aa588190ba3978fde0db5bbd |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aba67721788190951beae25e885457 |
completed | March 7, 2026, 4:15 a.m. |
| PD | Predicate disambiguation | batch_69aa61d514c081908197ac1f7c7d7a88 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:32 p.m.