Triple
T35693750
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nokia 3210 |
E1031373
|
entity |
| Predicate | hasSMSCapacity |
P123945
|
FINISHED |
| Object | up to 10 messages on 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: up to 10 messages on SIM | Statement: [Nokia 3210, hasSMSCapacity, up to 10 messages on SIM]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSMSCapacity Context triple: [Nokia 3210, hasSMSCapacity, up to 10 messages on SIM]
-
A.
smsCapacity
chosen
Indicates the maximum number of SMS messages that can be stored, sent, or handled within a given system or context.
-
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.
hasCapacityTo
Indicates that one entity possesses the ability, power, or potential to perform an action or bring about a particular effect in relation to another entity or context.
-
D.
operatorCapacity
Indicates the maximum workload or volume of tasks that an operator is able to handle within a given context or time frame.
-
E.
hasStandingCapacity
Indicates that an entity is capable of maintaining or supporting a specified condition, function, or load on an ongoing basis.
- 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_69f76e0c73ec819080ab60a9e2f5f1f6 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7a34f8ee08190a040304635539a8f |
completed | May 3, 2026, 7:34 p.m. |
| PD | Predicate disambiguation | batch_69f7a06f125c8190843af194f042a465 |
completed | May 3, 2026, 7:22 p.m. |
Created at: May 3, 2026, 4:05 p.m.