Triple
T14387864
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fahrenheit |
E356770
|
entity |
| Predicate | waterTriplePointApproximation |
P114046
|
FINISHED |
| Object | 32.018 degrees Fahrenheit |
—
|
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: 32.018 degrees Fahrenheit | Statement: [Fahrenheit, waterTriplePointApproximation, 32.018 degrees Fahrenheit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: waterTriplePointApproximation Context triple: [Fahrenheit, waterTriplePointApproximation, 32.018 degrees Fahrenheit]
-
A.
boilingPoint
Indicates the temperature at which a substance changes from liquid to gas under specified pressure conditions.
-
B.
meltingPoint
Indicates the temperature at which a substance changes from solid to liquid under specified conditions.
-
C.
hasTypicalFreezingPoint
Indicates the temperature at which a substance normally changes from liquid to solid under standard conditions.
-
D.
equilibriumTemperature
Indicates the temperature at which a system’s heat exchange balances so that no net change in its thermal state occurs.
-
E.
waterTemperatureType
Indicates the classification or category of a water body’s temperature (e.g., cold, warm, hot) associated with an entity or context.
- 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_69d827927c988190ad98bb0360981783 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de90283b9c8190b50d30ad58bfe085 |
completed | April 14, 2026, 7:06 p.m. |
| PD | Predicate disambiguation | batch_69de2aa024c48190805df6a9d63deb10 |
completed | April 14, 2026, 11:53 a.m. |
| PDg | Predicate description generation | batch_69de2e08b6c08190bb4c929deab236a6 |
completed | April 14, 2026, 12:07 p.m. |
Created at: April 10, 2026, 1:16 a.m.