Triple
T26100478
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mexican telephone numbering plan |
E658384
|
entity |
| Predicate | usesAreaCodes |
P148673
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Mexican telephone numbering plan, usesAreaCodes, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesAreaCodes Context triple: [Mexican telephone numbering plan, usesAreaCodes, yes]
-
A.
hasAreaCode
Indicates that a specified telephone area code is assigned to or associated with a particular geographic region, location, or phone service entity.
-
B.
hasAreaCodeType
Indicates that an entity’s area code is associated with a specific type or classification of area code.
-
C.
hasAreaCodeCountry
Indicates that a particular telephone area code is associated with or belongs to a specific country.
-
D.
usesVariableLengthAreaCodes
Indicates that the telephone numbering plan in question employs area codes whose lengths are not fixed but can vary.
-
E.
requiresAreaCodeForLocalCalls
chosen
Indicates that making local telephone calls in the given context necessitates dialing the area code.
- 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_69ee5bc09c288190bc42a11972841383 |
completed | April 26, 2026, 6:38 p.m. |
| NER | Named-entity recognition | batch_69f62d89b89c8190afb372a8172111e7 |
completed | May 2, 2026, 4:59 p.m. |
| PD | Predicate disambiguation | batch_69f62c1379f08190836c3e02b0c892df |
completed | May 2, 2026, 4:53 p.m. |
Created at: April 26, 2026, 7:54 p.m.