Triple
T22676677
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Salavan Province |
E560362
|
entity |
| Predicate | borderTypeWithVietnam |
P149203
|
FINISHED |
| Object | mountainous border |
—
|
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: mountainous border | Statement: [Salavan Province, borderTypeWithVietnam, mountainous border]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: borderTypeWithVietnam Context triple: [Salavan Province, borderTypeWithVietnam, mountainous border]
-
A.
borderTypeWithMongolia
Indicates the specific nature or classification of the border that an entity shares with Mongolia.
-
B.
borderTypeWithChina
Indicates the type or nature of the border relationship an entity has with China.
-
C.
borderTypeWithIndonesia
Indicates the type or nature of the border that an entity shares with Indonesia.
-
D.
borderTypeWithVenezuela
Indicates the specific type or nature of the border that an entity shares with Venezuela.
-
E.
countryBorderType
Indicates the type or nature of the border relationship that exists between two countries.
- 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_69e2454bfd00819099115715a22cb057 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1785ca1e08190af1a6cdb51ca4fce |
completed | April 29, 2026, 3:17 a.m. |
| PD | Predicate disambiguation | batch_69ee62a6245881909506ff502da14137 |
completed | April 26, 2026, 7:08 p.m. |
| PDg | Predicate description generation | batch_69ee8843d3308190b6e22bb98ae5c3d8 |
completed | April 26, 2026, 9:48 p.m. |
Created at: April 17, 2026, 3:11 p.m.