Triple
T22514905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Italian telephone numbering plan |
E556614
|
entity |
| Predicate | areaCodeExample |
P223
|
FINISHED |
| Object | 011 |
—
|
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: 011 | Statement: [Italian telephone numbering plan, areaCodeExample, 011]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areaCodeExample Context triple: [Italian telephone numbering plan, areaCodeExample, 011]
-
A.
areaCode
chosen
Indicates that a location, phone number, or region is associated with a specific telephone area code.
-
B.
callingCode
Indicates the telephone country or area code associated with an entity for making phone calls.
-
C.
hasAreaCode
Indicates that a specified telephone area code is assigned to or associated with a particular geographic region, location, or phone service entity.
-
D.
hasAreaCodeCountry
Indicates that a particular telephone area code is associated with or belongs to a specific country.
-
E.
cityCode
Indicates the standardized code that uniquely identifies a particular city.
- 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_69e11e555edc81909ca803587dafd747 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15e2c3098819098a553133cc9515b |
completed | April 29, 2026, 1:26 a.m. |
| PD | Predicate disambiguation | batch_69ee625e3b408190a60c759fb0b28fe2 |
completed | April 26, 2026, 7:07 p.m. |
Created at: April 16, 2026, 8:50 p.m.