Triple
T2508958
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 4DX auditoriums |
E52654
|
entity |
| Predicate | effectControl |
P30853
|
FINISHED |
| Object | centralized computer system |
—
|
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: centralized computer system | Statement: [4DX auditoriums, effectControl, centralized computer system]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectControl Context triple: [4DX auditoriums, effectControl, centralized computer system]
-
A.
exportControl
Indicates that an entity is subject to rules or restrictions governing the transfer or export of goods, services, or information across borders.
-
B.
visualEffect
Indicates that one entity produces, modifies, or is associated with a particular visual effect on another entity or within a scene.
-
C.
canControl
chosen
Indicates that one entity has the ability or authority to direct, manage, or influence the behavior or state of another entity.
-
D.
controlFrom
Indicates that one entity exercises authority, influence, or regulatory power over another entity or process.
-
E.
specialEffectsBy
Indicates that the special effects for something (such as a film, scene, or shot) are created or provided by a particular person 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_69ab4958e76481908a235377dd921c9e |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd65d6a988190aaaac8e98540a14f |
completed | March 7, 2026, 7:40 a.m. |
| PD | Predicate disambiguation | batch_69abd0bd996c8190ba8b9d6e4333b8d4 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:46 p.m.