Triple
T4175187
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Onega |
E86458
|
entity |
| Predicate | rankInEuropeByArea |
P17043
|
FINISHED |
| Object | one of the largest lakes in Europe |
—
|
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 lakes in Europe | Statement: [Lake Onega, rankInEuropeByArea, one of the largest lakes in Europe]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankInEuropeByArea Context triple: [Lake Onega, rankInEuropeByArea, one of the largest lakes in Europe]
-
A.
areaRankingInEurope
chosen
Indicates the position of an entity in a size-based ranking of areas within Europe.
-
B.
rankInRussiaByArea
Indicates the position of an entity in an ordered list of entities in Russia sorted by their area size.
-
C.
rankInGermanEmpireByArea
Indicates the ordinal position of an entity when all entities in the German Empire are ordered by their land area.
-
D.
rankInWorldByArea
Indicates the position of an entity in a global ordering based on its total area size.
-
E.
areaOfMemberStatesApprox
Indicates the approximate total geographic area collectively covered by the member states of a given organization or grouping.
- 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_69aed93de98c8190ad838ce507b77c8a |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af07078cb081909f64326b12522410 |
completed | March 9, 2026, 5:44 p.m. |
| PD | Predicate disambiguation | batch_69af019155448190b19868583272513f |
completed | March 9, 2026, 5:21 p.m. |
Created at: March 9, 2026, 3:45 p.m.