Triple
T407031
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eurasia |
E9404
|
entity |
| Predicate | isLargestContiguousLandmass |
P11013
|
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: [Eurasia, isLargestContiguousLandmass, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isLargestContiguousLandmass Context triple: [Eurasia, isLargestContiguousLandmass, true]
-
A.
hasLargestCountryByArea
Indicates that, among a set of compared entities, the subject is associated with the country that has the greatest land area.
-
B.
continent
Indicates that one entity is a continent on which the other entity is geographically located or to which it belongs.
-
C.
largestStateByArea
Indicates that a state is the one with the greatest land area within a specified set or region.
-
D.
continentScope
Indicates that something applies within, is limited to, or is defined at the level of a specific continent.
-
E.
sharesLandmassWith
Indicates that two geographic entities are located on the same continuous landmass or continent.
- 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_69a2e80111fc8190961d5b7c6154123f |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2ecbd766c8190bb8a91605929156a |
completed | Feb. 28, 2026, 1:25 p.m. |
| PD | Predicate disambiguation | batch_69a2e971a3a481909e6b075f25dd234a |
completed | Feb. 28, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69a2ea4545608190898436c72e10f39d |
completed | Feb. 28, 2026, 1:14 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.