Triple
T4952237
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Green Wall |
E111193
|
entity |
| Predicate | hasPlannedWidth |
P13004
|
FINISHED |
| Object | approximately 15 kilometers in some concepts |
—
|
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 15 kilometers in some concepts | Statement: [Great Green Wall, hasPlannedWidth, approximately 15 kilometers in some concepts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPlannedWidth Context triple: [Great Green Wall, hasPlannedWidth, approximately 15 kilometers in some concepts]
-
A.
hasWidth
Indicates that an entity possesses a specific measurement or extent along its width dimension.
-
B.
plannedHeight
Indicates the intended or designed vertical size or elevation that something is planned to have.
-
C.
hasApproximateMaximumWidth
chosen
Indicates that an entity’s maximum width is known only approximately, rather than as an exact value.
-
D.
sectionWidthGranted
Indicates that a specific width has been allocated or approved for a section within a layout or structure.
-
E.
hasLayout
Indicates that one entity defines or is associated with the structural arrangement or organization (layout) of another entity.
- 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_69bd4418390c8190b7e9766a2512ce55 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd71b6a5d481909ad6f5e0b752496c |
completed | March 20, 2026, 4:11 p.m. |
| PD | Predicate disambiguation | batch_69bd6c3aa1388190b3e0c8ee1ba1e4fa |
completed | March 20, 2026, 3:48 p.m. |
Created at: March 20, 2026, 1:31 p.m.