Triple
T2068733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Đan Phượng District |
E45965
|
entity |
| Predicate | hasAreaCodeSystem |
P35165
|
FINISHED |
| Object | Vietnamese telephone numbering plan |
—
|
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: Vietnamese telephone numbering plan | Statement: [Đan Phượng District, hasAreaCodeSystem, Vietnamese telephone numbering plan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAreaCodeSystem Context triple: [Đan Phượng District, hasAreaCodeSystem, Vietnamese telephone numbering plan]
-
A.
hasAreaCodeType
Indicates that an entity’s area code is associated with a specific type or classification of area code.
-
B.
hasAreaCode
Indicates that a specified telephone area code is assigned to or associated with a particular geographic region, location, or phone service entity.
-
C.
areaCode
Indicates that a location, phone number, or region is associated with a specific telephone area code.
-
D.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
E.
alsoServedByPostalCodeSystem
Indicates that the same geographic area or address is additionally covered or identified by another postal code system.
- F. None of above. chosen
Provenance (4 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_69a8891b38288190abd572ccad9b6928 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb9f51a008190aead0173a9289204 |
completed | March 7, 2026, 5:39 a.m. |
| PD | Predicate disambiguation | batch_69abb7aee9b48190999620176e3a6ee2 |
completed | March 7, 2026, 5:29 a.m. |
| PDg | Predicate description generation | batch_69abb87b9fc08190a748c278ef2d7dc7 |
completed | March 7, 2026, 5:32 a.m. |
Created at: March 4, 2026, 7:41 p.m.