Triple
T31472330
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Okeechobee |
E802891
|
entity |
| Predicate | rankInUSBySurfaceArea |
P181077
|
FINISHED |
| Object | one of the largest lakes in the United States |
—
|
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 the United States | Statement: [Lake Okeechobee, rankInUSBySurfaceArea, one of the largest lakes in the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankInUSBySurfaceArea Context triple: [Lake Okeechobee, rankInUSBySurfaceArea, one of the largest lakes in the United States]
-
A.
rankInWorldByArea
Indicates the position of an entity in a global ordering based on its total area size.
-
B.
continentRankByArea
Indicates the relative position of a continent in an ordered list based on its total land area.
-
C.
modernCountryOfArea
Indicates that a specified area or region is currently located within, or administered by, a particular modern country.
-
D.
isLargestByArea
Indicates that one entity has the greatest area compared to all other entities in a specified set or context.
-
E.
regionRankBySize
Indicates the relative ordering of regions based on their physical size, from largest to smallest (or vice versa).
- 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_69f348c84c1c81908739f100ecf7394e |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f760a35b988190904e6267553ad2fe |
completed | May 3, 2026, 2:50 p.m. |
| PD | Predicate disambiguation | batch_69f75eb3d6f081908c933474eb359e3d |
completed | May 3, 2026, 2:41 p.m. |
| PDg | Predicate description generation | batch_69f760a2a90c8190b8fbc55412ab752b |
completed | May 3, 2026, 2:50 p.m. |
Created at: April 30, 2026, 9:27 p.m.