Triple
T227362
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Congo River mouth |
E4339
|
entity |
| Predicate | salinityGradient |
P9312
|
FINISHED |
| Object | freshwater to marine |
—
|
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: freshwater to marine | Statement: [Congo River mouth, salinityGradient, freshwater to marine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: salinityGradient Context triple: [Congo River mouth, salinityGradient, freshwater to marine]
-
A.
salinity
Indicates the concentration of dissolved salts present in or affecting something, typically a body of water or environment.
-
B.
hasHigherSalinityThan
Indicates that one entity has a greater concentration of dissolved salts than another entity.
-
C.
hasSalinityRange
Indicates the range of salinity values within which something (such as a substance, environment, or organism) is present, applicable, or able to function.
-
D.
waterMassType
Indicates the classification of a body of water according to its physical or compositional type.
-
E.
isShallowSea
Indicates that a body of water is a shallow marine area, typically near coasts or continental shelves, rather than deep ocean.
- F. None of above. chosen
Provenance (4 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_69a257363ffc81909757bde7ab3404da |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25d10ac248190a98dedabf5358668 |
completed | Feb. 28, 2026, 3:12 a.m. |
| PD | Predicate disambiguation | batch_69a25b5877588190af694d060377f027 |
completed | Feb. 28, 2026, 3:04 a.m. |
| PDg | Predicate description generation | batch_69a25d0ec71081908478c800be4f7bb0 |
completed | Feb. 28, 2026, 3:12 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.