Triple
T16975331
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barkly Region |
E411795
|
entity |
| Predicate | isVastArea |
P104266
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Barkly Region, isVastArea, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isVastArea Context triple: [Barkly Region, isVastArea, true]
-
A.
hasVastRemoteAreas
Indicates that a place or region contains large, sparsely populated or difficult-to-access areas.
-
B.
hasLargeArea
chosen
Indicates that an entity occupies or covers a spatial region whose size exceeds a specified large-area threshold.
-
C.
hasMacroArea
Indicates that one entity belongs to, or is located within, a broader geographic or conceptual macro-area represented by another entity.
-
D.
hasLargestAreaOf
Indicates that the subject entity possesses the greatest area (size of surface or region) compared to the other entities in the specified set or context.
-
E.
hasAreaRange
Indicates that something’s area falls within a specified minimum-to-maximum range.
- 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_69d886ca8f348190812768ea8d5055ce |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d18300c8819080c8bf19962754ba |
completed | April 18, 2026, 6:46 p.m. |
| PD | Predicate disambiguation | batch_69e35d4dff4881909b384e30f2d36bff |
completed | April 18, 2026, 10:30 a.m. |
Created at: April 10, 2026, 5:31 a.m.