Triple
T7854346
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Green Lake, Wisconsin |
E182133
|
entity |
| Predicate | hasLakeDepth |
P35925
|
FINISHED |
| Object | one of the deepest natural inland lakes in Wisconsin |
—
|
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 deepest natural inland lakes in Wisconsin | Statement: [Green Lake, Wisconsin, hasLakeDepth, one of the deepest natural inland lakes in Wisconsin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLakeDepth Context triple: [Green Lake, Wisconsin, hasLakeDepth, one of the deepest natural inland lakes in Wisconsin]
-
A.
lakeMaximumDepth
chosen
Indicates the greatest recorded vertical distance from the lake’s surface to its deepest point.
-
B.
hasWaterDepthCategory
Indicates the classification of something based on the range or category of its water depth.
-
C.
maximumWaterDepth
Indicates the greatest depth of water present or allowed in a given context, such as a location, container, or body of water.
-
D.
isHighAltitudeLake
Indicates that a lake is situated at a relatively high elevation above sea level.
-
E.
riverbedDepth
Indicates the depth or vertical distance from the water surface to the bottom of a river at a given location or time.
- 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_69ca82869ee08190b8f9040dbc2c0467 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb1a72cfdc8190a3186c4c2894f571 |
completed | March 31, 2026, 12:50 a.m. |
| PD | Predicate disambiguation | batch_69cae92180f88190ae3d44c3de7adc93 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:51 p.m.