Triple
T1639232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Katavi Region |
E35428
|
entity |
| Predicate | hasWildlifeAttraction |
P22447
|
FINISHED |
| Object | large populations of hippos |
—
|
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: large populations of hippos | Statement: [Katavi Region, hasWildlifeAttraction, large populations of hippos]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWildlifeAttraction Context triple: [Katavi Region, hasWildlifeAttraction, large populations of hippos]
-
A.
hasBiodiversityFeature
chosen
Indicates that an entity possesses or is associated with a specific biodiversity-related characteristic, attribute, or element.
-
B.
hasNearbyWildernessArea
Indicates that a wilderness area is located within a close geographic proximity to the referenced place or entity.
-
C.
hasZoo
Indicates that one entity possesses, operates, or is associated with a zoo.
-
D.
hasAnimal
Indicates that one entity possesses, keeps, or is associated with an animal.
-
E.
hasEndemicSpecies
Indicates that a place or region contains species that are native to and found only within that specific geographic area.
- 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_69a88604618c81908b41f6429c431eb6 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a96083e7308190abbf025fe8e43abb |
completed | March 5, 2026, 10:52 a.m. |
| PD | Predicate disambiguation | batch_69a907cac610819083cafd4396b6d66c |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:28 p.m.