Triple
T38700782
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nokia Asha 500 |
E950133
|
entity |
| Predicate | hasDualSIMVariant |
P99162
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Nokia Asha 500, hasDualSIMVariant, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDualSIMVariant Context triple: [Nokia Asha 500, hasDualSIMVariant, yes]
-
A.
supportsDualSIM
chosen
Indicates that one entity provides or enables the use of two SIM cards simultaneously or interchangeably in another entity.
-
B.
hasSIMType
Indicates that an entity uses or is associated with a specific type or category of SIM (Subscriber Identity Module).
-
C.
hasDualSpace
Indicates that one mathematical space is the dual space consisting of all linear functionals defined on another space.
-
D.
hasCellularComponent
Indicates that an entity possesses, includes, or is associated with a specific cellular component as part of its structure or organization.
-
E.
hasCellularModel
Indicates that one entity serves as a cellular (cell-based) model or system used to study, represent, or simulate the biological properties or behavior of another 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_69f76f0124408190bb39c3040734846b |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fcdfbc71c481908ba7f87907b17782 |
completed | May 7, 2026, 6:53 p.m. |
| PD | Predicate disambiguation | batch_69fcdbe580b8819087f143596b2c79c0 |
completed | May 7, 2026, 6:37 p.m. |
Created at: May 3, 2026, 4:33 p.m.