Triple
T390848
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Russian SFSR |
E8875
|
entity |
| Predicate | areaRankingInUSSR |
P1170
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Russian SFSR, areaRankingInUSSR, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areaRankingInUSSR Context triple: [Russian SFSR, areaRankingInUSSR, 1]
-
A.
areaOfMemberStatesApprox
Indicates the approximate total geographic area collectively covered by the member states of a given organization or grouping.
-
B.
continentRankByArea
Indicates the relative position of a continent in an ordered list based on its total land area.
-
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.
hasLargestCountryByArea
Indicates that, among a set of compared entities, the subject is associated with the country that has the greatest land area.
-
E.
areaRank
chosen
Indicates the relative ordering or position of an entity based on the size of its area compared to others.
- 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_69a2e7f55c60819097aff65ea2ca2832 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec5d73e881909101308a583c8f13 |
completed | Feb. 28, 2026, 1:23 p.m. |
| PD | Predicate disambiguation | batch_69a2e96960608190bdd342da9c5ddb5e |
completed | Feb. 28, 2026, 1:11 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.