Triple
T7188625
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | kelvin |
E167630
|
entity |
| Predicate | exampleFreezingPointOfWater |
P19716
|
FINISHED |
| Object | approximately 273.15 K at 1 atm |
—
|
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: approximately 273.15 K at 1 atm | Statement: [kelvin, exampleFreezingPointOfWater, approximately 273.15 K at 1 atm]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: exampleFreezingPointOfWater Context triple: [kelvin, exampleFreezingPointOfWater, approximately 273.15 K at 1 atm]
-
A.
boilingPoint
Indicates the temperature at which a substance changes from liquid to gas under specified pressure conditions.
-
B.
meltingPoint
chosen
Indicates the temperature at which a substance changes from solid to liquid under specified conditions.
-
C.
freezesOver
Indicates that a liquid surface becomes solid due to low temperatures, typically forming a layer of ice over it.
-
D.
waterTemperatureType
Indicates the classification or category of a water body’s temperature (e.g., cold, warm, hot) associated with an entity or context.
-
E.
typicalTemperatureScale
Indicates the temperature scale (such as Celsius or Fahrenheit) that is normally used to express temperature values for the given entity or context.
- 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_69c6888b5248819090499a884ee3ec39 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e8e3d9188190ba2792098d76fb86 |
completed | March 27, 2026, 8:30 p.m. |
| PD | Predicate disambiguation | batch_69c6e752385c819096fbab55566ee2a8 |
completed | March 27, 2026, 8:23 p.m. |
Created at: March 27, 2026, 2:50 p.m.