Triple
T21391643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Katsina-Ala |
E527666
|
entity |
| Predicate | distanceFromMakurdi |
P144037
|
FINISHED |
| Object | approximately 120 km by road |
—
|
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 120 km by road | Statement: [Katsina-Ala, distanceFromMakurdi, approximately 120 km by road]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromMakurdi Context triple: [Katsina-Ala, distanceFromMakurdi, approximately 120 km by road]
-
A.
distanceToAbuja
Indicates the spatial distance between a given location or entity and the city of Abuja.
-
B.
distanceFromBeninCity_km
Indicates the distance, measured in kilometers, between a given location and Benin City.
-
C.
distanceFromCotonou
Indicates the measured spatial distance between a given location and the city of Cotonou.
-
D.
distanceFromNairobi
Indicates the spatial distance between a given entity’s location and the city of Nairobi.
-
E.
distanceFromMackay
Indicates the spatial distance between a given entity or location and Mackay.
- 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_69e0b51ff3748190935c0a513c62a12b |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69ee62cbfef08190a33ac1f198c82cd0 |
completed | April 26, 2026, 7:09 p.m. |
| PD | Predicate disambiguation | batch_69e6162bbfc88190a3e75859941b2638 |
completed | April 20, 2026, 12:03 p.m. |
| PDg | Predicate description generation | batch_69e61b3e47f881908fb2aac9bd2bfb58 |
completed | April 20, 2026, 12:25 p.m. |
Created at: April 16, 2026, 5:13 p.m.