Triple
T60682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Superior |
E1205
|
entity |
| Predicate | isWorldRankByArea |
P1170
|
FINISHED |
| Object | largest freshwater lake by surface area |
—
|
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: largest freshwater lake by surface area | Statement: [Lake Superior, isWorldRankByArea, largest freshwater lake by surface area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isWorldRankByArea Context triple: [Lake Superior, isWorldRankByArea, largest freshwater lake by surface area]
-
A.
continentRankByArea
Indicates the relative position of a continent in an ordered list based on its total land area.
-
B.
populationRank
Indicates the relative position of an entity in an ordered list based on the size of its population.
-
C.
hasLargestCountryByArea
Indicates that, among a set of compared entities, the subject is associated with the country that has the greatest land area.
-
D.
areaRank
chosen
Indicates the relative ordering or position of an entity based on the size of its area compared to others.
-
E.
hasPopulationRank
Indicates the relative position of an entity in an ordered list based on the size of its population.
- 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_69a24ba4f760819081f6638a3c70538a |
completed | Feb. 28, 2026, 1:57 a.m. |
| NER | Named-entity recognition | batch_69a251a1b8ac8190b44be4c3c41e5681 |
completed | Feb. 28, 2026, 2:23 a.m. |
| PD | Predicate disambiguation | batch_69a24ea0bec48190b2af1fb287e9e692 |
completed | Feb. 28, 2026, 2:10 a.m. |
Created at: Feb. 28, 2026, 2:02 a.m.