Triple
T37937661
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vioolsdrif border post |
E946392
|
entity |
| Predicate | hasCounterpartBorderPost |
P39987
|
FINISHED |
| Object | Noordoewer border post |
E946391
|
NE 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: Noordoewer border post | Statement: [Vioolsdrif border post, hasCounterpartBorderPost, Noordoewer border post]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCounterpartBorderPost Context triple: [Vioolsdrif border post, hasCounterpartBorderPost, Noordoewer border post]
-
A.
hasBorderPostWith
chosen
Indicates that two regions or territories share a border where an official border post or checkpoint is located between them.
-
B.
connectsToBorderPost
Indicates that one entity is linked or leads directly to a border post, establishing a route or connection between them.
-
C.
adjacentCountryBorderPost
Indicates that a border post is located at or associated with the boundary between two adjacent countries.
-
D.
hasBorderCrossingSide
Indicates that one side of a border crossing is associated with or located on a particular boundary or segment of that crossing.
-
E.
hasBorderCrossingsType
Indicates that a border crossing is classified by a specific type or category of crossing (e.g., road, rail, pedestrian, maritime).
- F. None of above.
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_69f76ef531ac8190ae6d99e5786e76ec |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a4117f3409c8190952967c5ef4892ef |
completed | June 28, 2026, 12:47 p.m. |
| PD | Predicate disambiguation | batch_6a037a192a008190a9917688a9e804f4 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:20 p.m.