Triple
T2961654
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Soap Lake, Washington |
E80062
|
entity |
| Predicate | lakeWaterCharacteristic |
P27141
|
FINISHED |
| Object | high mineral content |
—
|
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: high mineral content | Statement: [Soap Lake, Washington, lakeWaterCharacteristic, high mineral content]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lakeWaterCharacteristic Context triple: [Soap Lake, Washington, lakeWaterCharacteristic, high mineral content]
-
A.
hydrologicalCharacteristic
Indicates a relationship where a hydrological feature or condition (such as water flow, level, or behavior) characterizes or describes another entity.
-
B.
hasWaterClarity
Indicates the degree to which water in a given context is clear, transparent, or free from visible impurities.
-
C.
hydrologyFeature
Indicates a relationship where one entity is a hydrological feature (such as a body or flow of water) associated with or characterizing another entity.
-
D.
waterCondition
chosen
Indicates the state or quality of water affecting an entity, such as its cleanliness, safety, or suitability for a particular use.
-
E.
lakeShape
Indicates the geometric or physical outline/form that a lake possesses.
- 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_69ad8b1341848190bd19dbf46892887d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9955e6488190bea170724d5fbfe8 |
completed | March 8, 2026, 3:44 p.m. |
| PD | Predicate disambiguation | batch_69ad960c5c8881909d679912bd7d78f3 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 2:57 p.m.