Triple
T2991484
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Skaneateles Lake |
E80762
|
entity |
| Predicate | waterQualityClassification |
P27141
|
FINISHED |
| Object | one of the cleanest lakes in New York State |
—
|
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 cleanest lakes in New York State | Statement: [Skaneateles Lake, waterQualityClassification, one of the cleanest lakes in New York State]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: waterQualityClassification Context triple: [Skaneateles Lake, waterQualityClassification, one of the cleanest lakes in New York State]
-
A.
hasWaterClarity
Indicates the degree to which water in a given context is clear, transparent, or free from visible impurities.
-
B.
waterQualityUse
Indicates the way in which water quality is evaluated, classified, or applied for specific purposes or uses.
-
C.
waterCondition
chosen
Indicates the state or quality of water affecting an entity, such as its cleanliness, safety, or suitability for a particular use.
-
D.
waterQualityIssues
Indicates that there are problems or concerns with the condition, safety, or suitability of a water source.
-
E.
waterType
Indicates the specific kind or category of water associated with an entity (e.g., fresh, salt, brackish).
- 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_69ad8b16c3488190b47b6aa7a59a335b |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad99df69d08190a0e25efb0dc8d653 |
completed | March 8, 2026, 3:46 p.m. |
| PD | Predicate disambiguation | batch_69ad961403108190bbecb8d3608fd4e0 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 2:59 p.m.