Triple
T21380909
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jones Lake |
E527350
|
entity |
| Predicate | surfaceWaterColorCause |
P96903
|
FINISHED |
| Object | tannins from surrounding vegetation |
—
|
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: tannins from surrounding vegetation | Statement: [Jones Lake, surfaceWaterColorCause, tannins from surrounding vegetation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: surfaceWaterColorCause Context triple: [Jones Lake, surfaceWaterColorCause, tannins from surrounding vegetation]
-
A.
waterColor
Indicates that one entity is the color or hue characteristic of water associated with another entity.
-
B.
colorOfLagoonsCausedBy
chosen
Indicates that the color observed in lagoons is caused or determined by the specified factor or agent.
-
C.
surfaceWater
Indicates that one entity consists of or contains surface-level water associated with another entity.
-
D.
hasWaterColor
Indicates that an entity possesses or is characterized by a particular color of water.
-
E.
usesSurfaceWater
Indicates that one entity relies on or draws from surface water sources (such as rivers, lakes, or reservoirs) for its activities or needs.
- 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_69e0b51f363c8190944000ab5523b02b |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b0cec99881908481d0b08df358a8 |
completed | April 22, 2026, 11:28 a.m. |
| PD | Predicate disambiguation | batch_69e6162bbfc88190a3e75859941b2638 |
completed | April 20, 2026, 12:03 p.m. |
Created at: April 16, 2026, 5:11 p.m.