Triple
T775423
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Higashi Shina Kai |
E16375
|
entity |
| Predicate | hasSurfaceWaterTemperatureRangeCelsius |
P9975
|
FINISHED |
| Object | 9–28 |
—
|
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: 9–28 | Statement: [Higashi Shina Kai, hasSurfaceWaterTemperatureRangeCelsius, 9–28]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSurfaceWaterTemperatureRangeCelsius Context triple: [Higashi Shina Kai, hasSurfaceWaterTemperatureRangeCelsius, 9–28]
-
A.
surfaceTemperatureRange
chosen
Indicates the range between the minimum and maximum surface temperatures observed or allowed for an entity.
-
B.
waterTemperatureType
Indicates the classification or category of a water body’s temperature (e.g., cold, warm, hot) associated with an entity or context.
-
C.
hasTemperature
Indicates that an entity possesses or is characterized by a specific temperature value.
-
D.
hasSalinityRange
Indicates the range of salinity values within which something (such as a substance, environment, or organism) is present, applicable, or able to function.
-
E.
hasTidalRange
Indicates the relationship between a location or body of water and the magnitude of difference between its high and low tide levels.
- 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_69a49369a0848190af883934cee3db4c |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a74da7648190adfad56717d564df |
completed | March 1, 2026, 8:53 p.m. |
| PD | Predicate disambiguation | batch_69a4a50a443481909ae3662764ee69a4 |
completed | March 1, 2026, 8:43 p.m. |
Created at: March 1, 2026, 7:37 p.m.