Triple
T31283106
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sa Kaeo Province |
E797727
|
entity |
| Predicate | borderTypeWithCambodia |
P197904
|
FINISHED |
| Object | 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: land border | Statement: [Sa Kaeo Province, borderTypeWithCambodia, land border]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: borderTypeWithCambodia Context triple: [Sa Kaeo Province, borderTypeWithCambodia, land border]
-
A.
borderTypeWithVietnam
Indicates the type or nature of the border relationship that an entity shares with Vietnam.
-
B.
borderTypeWithMongolia
Indicates the specific nature or classification of the border that an entity shares with Mongolia.
-
C.
borderTypeWithBangladesh
Indicates the type or nature of the border relationship that an entity shares with Bangladesh.
-
D.
borderTypeWithChina
Indicates the type or nature of the border relationship an entity has with China.
-
E.
borderTypeWithIndonesia
Indicates the type or nature of the border that an entity shares with Indonesia.
- 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_69f224def9088190a37034eab3daf57f |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69feb8e856d48190aa34ad8ee8376e1c |
completed | May 9, 2026, 4:32 a.m. |
| PD | Predicate disambiguation | batch_69feb82a2b6c8190a473cc25976897be |
completed | May 9, 2026, 4:29 a.m. |
| PDg | Predicate description generation | batch_69feb8e6ed7881908446745c89c30784 |
completed | May 9, 2026, 4:32 a.m. |
Created at: April 29, 2026, 9:13 p.m.