Triple
T15205055
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Colesberg |
E363366
|
entity |
| Predicate | distanceToBloemfontein |
P117114
|
FINISHED |
| Object | approximately 220 km |
—
|
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 220 km | Statement: [Colesberg, distanceToBloemfontein, approximately 220 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToBloemfontein Context triple: [Colesberg, distanceToBloemfontein, approximately 220 km]
-
A.
distanceFromCapeTown
Indicates the measured distance between a given location and Cape Town.
-
B.
distanceFromPretoria
Indicates the spatial distance between a given entity or location and the city of Pretoria.
-
C.
distanceToDurban_km
Indicates the physical distance, measured in kilometers, between a given location and the city of Durban.
-
D.
distanceToJohannesburg_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and Johannesburg.
-
E.
distanceToNelspruit
Indicates the spatial distance between a given entity’s location and the location of Nelspruit.
- 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_69d85a0b78bc8190b6e5ad51a2c4cfc5 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e006b7964c8190bc8dc3444b94f15e |
completed | April 15, 2026, 9:44 p.m. |
| PD | Predicate disambiguation | batch_69deb97ee9d881908711dbe12a55283c |
completed | April 14, 2026, 10:02 p.m. |
| PDg | Predicate description generation | batch_69dec72059c08190a34f513a00185b08 |
completed | April 14, 2026, 11 p.m. |
Created at: April 10, 2026, 3:11 a.m.