Triple
T28393993
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South African telephone numbering plan |
E719233
|
entity |
| Predicate | areaCodeLength |
P159939
|
FINISHED |
| Object | 2 digits for most geographic areas |
—
|
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: 2 digits for most geographic areas | Statement: [South African telephone numbering plan, areaCodeLength, 2 digits for most geographic areas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areaCodeLength Context triple: [South African telephone numbering plan, areaCodeLength, 2 digits for most geographic areas]
-
A.
areaCodeLength
chosen
Indicates the number of digits or characters that make up a given area code.
-
B.
countryCodeLength
Indicates the number of characters that a given country code consists of.
-
C.
areaCode
Indicates that a location, phone number, or region is associated with a specific telephone area code.
-
D.
subscriberNumberLengthRange
Indicates the allowed minimum and maximum length range for a subscriber’s phone number in a given context.
-
E.
usesVariableLengthAreaCodes
Indicates that the telephone numbering plan in question employs area codes whose lengths are not fixed but can vary.
- 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_69eff6efd1b08190ae3cefd4f11388a2 |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69f64cee303081908e27fadd6ef248b1 |
completed | May 2, 2026, 7:13 p.m. |
| PD | Predicate disambiguation | batch_69f641e2f1708190b45b48d6a43c51d2 |
completed | May 2, 2026, 6:26 p.m. |
Created at: April 28, 2026, 1:15 a.m.