Triple
T227284
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benguela Current |
E4338
|
entity |
| Predicate | waterTemperatureType |
P9307
|
FINISHED |
| Object | cold |
—
|
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: cold | Statement: [Benguela Current, waterTemperatureType, cold]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: waterTemperatureType Context triple: [Benguela Current, waterTemperatureType, cold]
-
A.
waterType
Indicates the specific kind or category of water associated with an entity (e.g., fresh, salt, brackish).
-
B.
hasTemperature
Indicates that an entity possesses or is characterized by a specific temperature value.
-
C.
waterMassType
Indicates the classification of a body of water according to its physical or compositional type.
-
D.
averageTemperature
Indicates the typical or mean temperature value associated with an entity over a specified period or context.
-
E.
waterbodyType
Indicates the classification of a water body according to its type (e.g., river, lake, ocean, etc.).
- 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.