Triple
T10171191
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Afro-Eurasia |
E235333
|
entity |
| Predicate | containsMajorityOfWorldLandArea |
P92215
|
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: [Afro-Eurasia, containsMajorityOfWorldLandArea, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsMajorityOfWorldLandArea Context triple: [Afro-Eurasia, containsMajorityOfWorldLandArea, true]
-
A.
hasLandAreaRange
Indicates that an entity’s land area falls within a specified minimum-to-maximum range.
-
B.
hasLargestContinuousLandAreaOn
Indicates that an entity possesses the greatest uninterrupted expanse of land on a specified geographic region or surface compared to all other entities.
-
C.
isLargestContiguousLandmass
Indicates that one landmass is the largest single, unbroken continuous area of land compared to all other landmasses in the relevant context.
-
D.
hasLargestCountryByArea
Indicates that, among a set of compared entities, the subject is associated with the country that has the greatest land area.
-
E.
hasLandCoverage
Indicates that a specified area or region is covered or occupied by a particular type of land surface or land use.
- F. None of above. chosen
Provenance (4 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. |
| PDg | Predicate description generation | batch_69cd4fed19d481909d2c7ff1114664b6 |
completed | April 1, 2026, 5:03 p.m. |
Created at: March 30, 2026, 9:10 p.m.