Triple
T13496757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | German telephone numbering plan |
E320781
|
entity |
| Predicate | internationalCallingCode |
P247
|
FINISHED |
| Object | +49 |
—
|
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: +49 | Statement: [German telephone numbering plan, internationalCallingCode, +49]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: internationalCallingCode Context triple: [German telephone numbering plan, internationalCallingCode, +49]
-
A.
callingCode
chosen
Indicates the telephone country or area code associated with an entity for making phone calls.
-
B.
callingCodeType
Indicates the type or category of a telephone calling code associated with an entity.
-
C.
countryCodeMeaning
Indicates the semantic meaning or description associated with a given country code.
-
D.
UICCountryCode
Indicates that an entity is associated with a specific country identified by its UIC (International Union of Railways) country code.
-
E.
isCountryCode
Indicates that one entity is a valid country code designating the country represented by the other 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_69d807629d6c8190998f1b9bb12d2ed0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaf4e9ca4819083116890a65389f9 |
completed | April 12, 2026, 2:42 p.m. |
| PD | Predicate disambiguation | batch_69dbae06061881909a6a6032e0507587 |
completed | April 12, 2026, 2:36 p.m. |
Created at: April 9, 2026, 9:43 p.m.