Triple
T9704493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | American cinema |
E234863
|
entity |
| Predicate | hasGlobalMarketShare |
P90779
|
FINISHED |
| Object | high share of worldwide box office |
—
|
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: high share of worldwide box office | Statement: [American cinema, hasGlobalMarketShare, high share of worldwide box office]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGlobalMarketShare Context triple: [American cinema, hasGlobalMarketShare, high share of worldwide box office]
-
A.
hasGlobalReach
Indicates that an entity’s influence, operations, or impact extends across multiple countries or worldwide.
-
B.
hasMarketFocus
Indicates that an entity concentrates its business activities, products, or services on a particular market or customer segment.
-
C.
hasMarket
Indicates that an entity possesses, operates in, or is associated with a particular market or marketplace.
-
D.
hasGlobalBrand
Indicates that an entity possesses a brand that is recognized and operates across multiple countries or worldwide.
-
E.
hasGlobalDistribution
Indicates that the related entity occurs, operates, or is present across most or all regions of the world rather than being confined to a specific locality or region.
- 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_69ca84cc78808190a56f3402b7c139a7 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9d74afb4819084174aab5bcdb6e0 |
completed | April 1, 2026, 10:34 p.m. |
| PD | Predicate disambiguation | batch_69cd03b641408190942464eaf174c6b5 |
completed | April 1, 2026, 11:38 a.m. |
| PDg | Predicate description generation | batch_69cd081a9c5c819093439be7e802ff85 |
completed | April 1, 2026, 11:57 a.m. |
Created at: March 30, 2026, 8:18 p.m.