Triple
T23627166
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Volta Lake |
E583497
|
entity |
| Predicate | rankBySurfaceArea |
P17270
|
FINISHED |
| Object | one of the world’s largest artificial lakes |
—
|
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 world’s largest artificial lakes | Statement: [Volta Lake, rankBySurfaceArea, one of the world’s largest artificial lakes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankBySurfaceArea Context triple: [Volta Lake, rankBySurfaceArea, one of the world’s largest artificial lakes]
-
A.
rankInWorldByArea
chosen
Indicates the position of an entity in a global ordering based on its total area size.
-
B.
regionRankBySize
Indicates the relative ordering of regions based on their physical size, from largest to smallest (or vice versa).
-
C.
continentRankByArea
Indicates the relative position of a continent in an ordered list based on its total land area.
-
D.
rankingInCountryBySize
Indicates the position of an entity in an ordered list of entities within a specific country, based on their relative size.
-
E.
rankInArchipelagoByArea
Indicates the position of an island in a size-based ordering of islands within the same archipelago, based on their land area.
- 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_69e248fc8d74819091bd5baef2f36f6f |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b1e4df248190ac38e4e025bd1140 |
completed | April 29, 2026, 7:23 a.m. |
| PD | Predicate disambiguation | batch_69f118d0e0588190a86527a7747c5427 |
completed | April 28, 2026, 8:30 p.m. |
Created at: April 17, 2026, 6:46 p.m.