Triple
T15745699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Namanga |
E381715
|
entity |
| Predicate | distanceToAmboseliNationalPark |
P120471
|
FINISHED |
| Object | approximately 75 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 75 km | Statement: [Namanga, distanceToAmboseliNationalPark, approximately 75 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToAmboseliNationalPark Context triple: [Namanga, distanceToAmboseliNationalPark, approximately 75 km]
-
A.
distanceToKrugerNationalPark
Indicates the measured distance between a given location and Kruger National Park.
-
B.
distanceFromKrugerNationalParkMainGate
Indicates the measured distance between a given location and the main gate of Kruger National Park.
-
C.
distanceFromNairobi
Indicates the spatial distance between a given entity’s location and the city of Nairobi.
-
D.
distanceToArusha
Indicates the measured spatial distance between a given entity and the location Arusha.
-
E.
distanceToUluru
Indicates the spatial distance between a given entity and Uluru.
- 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_69d86d9e6b44819085d1f6a969ecb74c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0b4d6b5788190883746ee82c799f5 |
completed | April 16, 2026, 10:07 a.m. |
| PD | Predicate disambiguation | batch_69e0052c6208819098165d61d378d13b |
completed | April 15, 2026, 9:37 p.m. |
| PDg | Predicate description generation | batch_69e0b4d01c9c81909f6b611e8144c838 |
completed | April 16, 2026, 10:07 a.m. |
Created at: April 10, 2026, 4:46 a.m.