Triple
T17091558
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Hamgyong Province |
E414737
|
entity |
| Predicate | borderTypeWithRussia |
P125856
|
FINISHED |
| Object | short land 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: short land border | Statement: [North Hamgyong Province, borderTypeWithRussia, short land border]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: borderTypeWithRussia Context triple: [North Hamgyong Province, borderTypeWithRussia, short land border]
-
A.
borderTypeWithMongolia
Indicates the specific nature or classification of the border that an entity shares with Mongolia.
-
B.
borderTypeWithArmenia
Indicates the type or nature of the border relationship that an entity has with Armenia.
-
C.
countryBorderType
Indicates the type or nature of the border relationship that exists between two countries.
-
D.
borderTypeWithTurkmenistan
Indicates the type or nature of the border relationship that an entity has with Turkmenistan.
-
E.
borderTypeWithIran
Indicates the type or nature of the border relationship that an entity has with Iran.
- 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_69d886cfc8e88190b05ba466edd35591 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dbfa09b08190be4303dd0d174feb |
completed | April 18, 2026, 7:31 p.m. |
| PD | Predicate disambiguation | batch_69e35d67b14481909fcdbdeaa5c34785 |
completed | April 18, 2026, 10:31 a.m. |
| PDg | Predicate description generation | batch_69e37542d060819082aa73948eb8ebd4 |
completed | April 18, 2026, 12:12 p.m. |
Created at: April 10, 2026, 5:35 a.m.