Triple
T645637
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dolby Laboratories |
E11234
|
entity |
| Predicate | hasTechnologyIn |
P1485
|
FINISHED |
| Object | cinemas |
—
|
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: cinemas | Statement: [Dolby Laboratories, hasTechnologyIn, cinemas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTechnologyIn Context triple: [Dolby Laboratories, hasTechnologyIn, cinemas]
-
A.
hasTechnique
Indicates that an entity employs, utilizes, or is associated with a particular method, procedure, or technique.
-
B.
hasAdvancedTechnologySector
Indicates that an entity possesses or includes a developed sector focused on advanced or high-tech industries, products, or services.
-
C.
technologicalFeature
Indicates that one entity possesses, exhibits, or is characterized by a specific technological capability, component, or functionality in relation to another entity.
-
D.
hardwareUsedBy
Indicates that a piece of hardware is utilized or operated by a particular entity (such as a person, system, or organization).
-
E.
associatedWithTechnology
chosen
Indicates a relationship where an entity is connected to, involved with, or utilizes a particular technology.
- 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_69a493266a2881909daf4c40f719dee8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49f19f9a08190b0bf6e19b32427ff |
completed | March 1, 2026, 8:18 p.m. |
| PD | Predicate disambiguation | batch_69a49d0a0ab481909871461418a00be7 |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:36 p.m.