Triple
T2649147
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rybinsk Reservoir |
E53854
|
entity |
| Predicate | rankInRussiaByArea |
P41043
|
FINISHED |
| Object | one of the largest reservoirs in Russia |
—
|
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: one of the largest reservoirs in Russia | Statement: [Rybinsk Reservoir, rankInRussiaByArea, one of the largest reservoirs in Russia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankInRussiaByArea Context triple: [Rybinsk Reservoir, rankInRussiaByArea, one of the largest reservoirs in Russia]
-
A.
rankInWorldByArea
Indicates the position of an entity in a global ordering based on its total area size.
-
B.
largestMunicipalityByArea
Indicates that the subject is the municipality with the greatest land area within the specified region or set of municipalities.
-
C.
continentRankByArea
Indicates the relative position of a continent in an ordered list based on its total land area.
-
D.
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).
-
E.
areaTotalSquareKilometers
Indicates the total size of something measured in square kilometers.
- 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_69ab495e192081909c77b622e8e7e15a |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd91ca1288190ba302b04bac4c153 |
completed | March 7, 2026, 7:51 a.m. |
| PD | Predicate disambiguation | batch_69abd814298c8190952f05aed43f6bb8 |
completed | March 7, 2026, 7:47 a.m. |
| PDg | Predicate description generation | batch_69abd879bb808190bd2c34de1664c816 |
completed | March 7, 2026, 7:49 a.m. |
Created at: March 6, 2026, 9:53 p.m.