Triple
T3835219
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ad Dahna Desert |
E91113
|
entity |
| Predicate | hasTypicalVegetation |
P953
|
FINISHED |
| Object | sparse desert vegetation |
—
|
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: sparse desert vegetation | Statement: [Ad Dahna Desert, hasTypicalVegetation, sparse desert vegetation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalVegetation Context triple: [Ad Dahna Desert, hasTypicalVegetation, sparse desert vegetation]
-
A.
vegetationType
chosen
Indicates the specific kind or category of plant cover or flora that characterizes a given area or environment.
-
B.
vegetation
Indicates that an area or object is covered with, contains, or is characterized by plant life.
-
C.
hasAttractiveFoliage
Indicates that an entity possesses foliage that is visually appealing or ornamental in appearance.
-
D.
hasMeadow
Indicates that one entity possesses, contains, or includes a meadow as part of its area or composition.
-
E.
hasVegetationIssue
Indicates that an entity is affected by a problem, damage, or abnormal condition related to its vegetation or plant life.
- 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_69aed960b538819096561c8ed448dec9 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeeb9a27508190b05e5312cc7c8033 |
completed | March 9, 2026, 3:47 p.m. |
| PD | Predicate disambiguation | batch_69aee74dcecc819098285483ec721b40 |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:18 p.m.