Triple
T12890984
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nokia 3109 classic |
E308362
|
entity |
| Predicate | hasBluetoothProfile |
P39529
|
FINISHED |
| Object | A2DP |
—
|
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: A2DP | Statement: [Nokia 3109 classic, hasBluetoothProfile, A2DP]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBluetoothProfile Context triple: [Nokia 3109 classic, hasBluetoothProfile, A2DP]
-
A.
hasBluetooth
chosen
Indicates that one entity is equipped with or supports Bluetooth connectivity.
-
B.
hasProximitySensor
Indicates that an entity is equipped with a sensor capable of detecting nearby objects or measuring its distance to them.
-
C.
hasCellularComponent
Indicates that an entity possesses, includes, or is associated with a specific cellular component as part of its structure or organization.
-
D.
hasCharacteristic
Indicates that an entity possesses, exhibits, or is defined by a particular attribute, feature, or quality.
-
E.
hasHardware
Indicates that one entity possesses, includes, or is equipped with specific hardware components or devices.
- 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_69d7bdf7c1f0819098102569a8d8cbf5 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97146d2208190be5ae26e51193b67 |
completed | April 10, 2026, 9:53 p.m. |
| PD | Predicate disambiguation | batch_69d96fa776648190b9b5c30722ea50b6 |
completed | April 10, 2026, 9:46 p.m. |
Created at: April 9, 2026, 5:39 p.m.