Triple
T25575158
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lupane District |
E641084
|
entity |
| Predicate | hasNaturalVegetation |
P953
|
FINISHED |
| Object | savanna woodland |
—
|
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: savanna woodland | Statement: [Lupane District, hasNaturalVegetation, savanna woodland]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNaturalVegetation Context triple: [Lupane District, hasNaturalVegetation, savanna woodland]
-
A.
hasNature
Indicates that something possesses, exhibits, or is characterized by a particular inherent quality, essence, or fundamental type.
-
B.
hasNaturalAreaType
Indicates that an entity is associated with or classified by a specific type of natural area (e.g., forest, wetland, grassland).
-
C.
hasNaturalFeature
Indicates that one entity possesses, contains, or is characterized by a particular natural feature (such as a mountain, river, forest, or coastline).
-
D.
vegetationType
chosen
Indicates the specific kind or category of plant cover or flora that characterizes a given area or environment.
-
E.
hasNaturalProtection
Indicates that an entity is safeguarded by inherent or environmental protective features (such as terrain, vegetation, or natural barriers) rather than by artificial defenses.
- 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_69e75dc281bc819095ec04dc0c3a94d0 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f6691f5e188190b12c7b2eb729a45e |
completed | May 2, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69f66598d6008190a7ca8ff80399fd34 |
completed | May 2, 2026, 8:59 p.m. |
Created at: April 21, 2026, 4 p.m.