Triple
T21418976
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bergville |
E528380
|
entity |
| Predicate | distanceTo_Johannesburg_km |
P34439
|
FINISHED |
| Object | approximately 350 |
—
|
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: approximately 350 | Statement: [Bergville, distanceTo_Johannesburg_km, approximately 350]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceTo_Johannesburg_km Context triple: [Bergville, distanceTo_Johannesburg_km, approximately 350]
-
A.
distanceToJohannesburg_km
chosen
Indicates the physical distance, measured in kilometers, between a given entity’s location and Johannesburg.
-
B.
approximateDistanceToJohannesburg
Indicates that one entity has an estimated or rough distance relationship to the location Johannesburg.
-
C.
distanceToDurban_km
Indicates the physical distance, measured in kilometers, between a given location and the city of Durban.
-
D.
distanceFromPretoria
Indicates the spatial distance between a given entity or location and the city of Pretoria.
-
E.
distanceFromCapeTown
Indicates the measured distance between a given location and Cape Town.
- F. None of above.
Provenance (3 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_69e0c454c248819093425d1099101c09 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ee62d392e08190b7f378005afc9026 |
completed | April 26, 2026, 7:09 p.m. |
| PD | Predicate disambiguation | batch_69e61633f8208190a2a849457c4e4198 |
completed | April 20, 2026, 12:04 p.m. |
Created at: April 16, 2026, 5:47 p.m.