Triple
T10171194
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Afro-Eurasia |
E235333
|
entity |
| Predicate | containsWorldLandAreaShareApproximate |
P18214
|
FINISHED |
| Object | 0.57 |
—
|
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: 0.57 | Statement: [Afro-Eurasia, containsWorldLandAreaShareApproximate, 0.57]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsWorldLandAreaShareApproximate Context triple: [Afro-Eurasia, containsWorldLandAreaShareApproximate, 0.57]
-
A.
landAreaShare
chosen
Indicates the proportion of a larger geographic area’s total land area that is accounted for by a specific sub-area or unit.
-
B.
hasLandAreaRange
Indicates that an entity’s land area falls within a specified minimum-to-maximum range.
-
C.
landArea
Indicates the total surface area of a piece of land associated with an entity, typically measured in standardized units (e.g., square meters, hectares).
-
D.
hasLandAreaSquareKilometers
Indicates that an entity possesses a land area measured in square kilometers.
-
E.
sharesLandmassWith
Indicates that two geographic entities are located on the same continuous landmass or continent.
- 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_69ca84ceafd0819085828600e11bed6b |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdec9e4e0c819097dceb7bf7757948 |
completed | April 2, 2026, 4:12 a.m. |
| PD | Predicate disambiguation | batch_69cd4ba9956c8190a3e15d091e33149d |
completed | April 1, 2026, 4:45 p.m. |
Created at: March 30, 2026, 9:10 p.m.