Triple
T11751715
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nokia 130 |
E279421
|
entity |
| Predicate | hasSIMConfiguration |
P101151
|
FINISHED |
| Object | single SIM |
—
|
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: single SIM | Statement: [Nokia 130, hasSIMConfiguration, single SIM]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSIMConfiguration Context triple: [Nokia 130, hasSIMConfiguration, single SIM]
-
A.
hasCellularComponent
Indicates that an entity possesses, includes, or is associated with a specific cellular component as part of its structure or organization.
-
B.
hasCellService
Indicates that a location, device, or area is within range of a cellular network and can access mobile phone or data services.
-
C.
hasSimulator
Indicates that one entity provides or is associated with a simulator used to model, emulate, or test the behavior of another entity.
-
D.
supportsSIMInstruction
Indicates that one entity is capable of handling or executing a specified SIM (Subscriber Identity Module) instruction for another entity.
-
E.
supportsEsim
Indicates that one entity provides compatibility with, or the ability to use, an embedded SIM (eSIM) for another entity.
- 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_69d6ab01038c819080714901502c84fc |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a509c2448190b0deb7ed29c3a73f |
completed | April 10, 2026, 7:21 a.m. |
| PD | Predicate disambiguation | batch_69d88a813cc48190a3dfdc60e8af80ae |
completed | April 10, 2026, 5:28 a.m. |
| PDg | Predicate description generation | batch_69d890458d948190b15054c9ba0fd923 |
completed | April 10, 2026, 5:53 a.m. |
Created at: April 8, 2026, 9:41 p.m.