Triple
T12578082
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marshall County, South Dakota |
E300260
|
entity |
| Predicate | hasWaterAreaCharacteristic |
P64067
|
FINISHED |
| Object | many lakes and wetlands |
—
|
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: many lakes and wetlands | Statement: [Marshall County, South Dakota, hasWaterAreaCharacteristic, many lakes and wetlands]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWaterAreaCharacteristic Context triple: [Marshall County, South Dakota, hasWaterAreaCharacteristic, many lakes and wetlands]
-
A.
hasWaterBodyCharacteristic
Indicates that a water body possesses a specified physical, chemical, or ecological characteristic.
-
B.
hasWaterCharacteristics
Indicates that one entity possesses qualities, properties, or behaviors characteristic of water.
-
C.
hasAreaWaterBody
Indicates that an entity includes, contains, or is associated with a body of water within its area or boundaries.
-
D.
hasWaterFeatures
chosen
Indicates that an entity includes or is associated with water-related elements such as fountains, ponds, streams, or similar features.
-
E.
hasWatershedCharacteristic
Indicates that a watershed possesses a specified characteristic, feature, or property.
- 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_69d7bde87b648190bcd0266e9efde098 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9550d84908190aea0f50055f6d92e |
completed | April 10, 2026, 7:52 p.m. |
| PD | Predicate disambiguation | batch_69d95414692881909c52a1de7d224b44 |
completed | April 10, 2026, 7:48 p.m. |
Created at: April 9, 2026, 4:53 p.m.