Triple
T10382013
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Koudougou |
E244663
|
entity |
| Predicate | distanceFromOuagadougou_km |
P93888
|
FINISHED |
| Object | approximately 75 |
—
|
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 75 | Statement: [Koudougou, distanceFromOuagadougou_km, approximately 75]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromOuagadougou_km Context triple: [Koudougou, distanceFromOuagadougou_km, approximately 75]
-
A.
distanceFromBamako
Indicates the spatial distance between an entity and the location of Bamako.
-
B.
distanceFromCotonou
Indicates the measured spatial distance between a given location and the city of Cotonou.
-
C.
distanceFromDakar
Indicates the spatial distance between a given entity’s location and the city of Dakar.
-
D.
distanceToLomé
Indicates the spatial distance between a given entity or location and the city of Lomé.
-
E.
distanceToBujumbura_km
Indicates the physical distance, measured in kilometers, between a given place or entity and the city of Bujumbura.
- 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_69d381b3e328819094b23b8edcd29b5a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e992d8e08190aaa9a04925f52ccc |
completed | April 7, 2026, 11:25 a.m. |
| PD | Predicate disambiguation | batch_69d4dfb0e7a88190bec0b7a52c70dfe2 |
completed | April 7, 2026, 10:42 a.m. |
| PDg | Predicate description generation | batch_69d4e91ce2008190af252c140370b7f2 |
completed | April 7, 2026, 11:23 a.m. |
Created at: April 6, 2026, 12:04 p.m.