Triple
T3470444
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Butaro, Rwanda |
E73243
|
entity |
| Predicate | distanceToCountryCapital |
P10889
|
FINISHED |
| Object | approximately 80–100 km north of Kigali |
—
|
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 80–100 km north of Kigali | Statement: [Butaro, Rwanda, distanceToCountryCapital, approximately 80–100 km north of Kigali]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToCountryCapital Context triple: [Butaro, Rwanda, distanceToCountryCapital, approximately 80–100 km north of Kigali]
-
A.
distanceFromCapital
chosen
Indicates the measured distance between a given location and the capital city of its corresponding region or country.
-
B.
countryCapitalNearby
Indicates that a country’s capital city is geographically close to a specified location or entity.
-
C.
distanceToContinentApproximate
Indicates an approximate measure of how far something is from a specified continent.
-
D.
distanceToFrance
Indicates the spatial distance between a given entity and the country of France.
-
E.
stateCapitalProximity
Indicates the spatial closeness or distance between a state’s capital city and another specified location.
- 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_69ad85b2fed48190948c8765e453d270 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbb392f6481908b6ad0457b8cf421 |
completed | March 8, 2026, 6:08 p.m. |
| PD | Predicate disambiguation | batch_69adae07802c8190919c49b0e65b2797 |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:17 p.m.