Triple
T6938912
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stefan–Boltzmann constant |
E160622
|
entity |
| Predicate | temperatureExponent |
P60741
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Stefan–Boltzmann constant, temperatureExponent, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: temperatureExponent Context triple: [Stefan–Boltzmann constant, temperatureExponent, 4]
-
A.
temperatureDependenceExponent
chosen
Indicates how strongly a quantity or process changes in response to variations in temperature, typically expressed as an exponent in a temperature-dependent relationship.
-
B.
temperatureProportionalTo
Indicates that the temperature of one entity changes in direct proportion to the temperature of another entity.
-
C.
temperatureDependent
Indicates that the existence, intensity, or outcome of a relationship or process varies as a function of temperature.
-
D.
temperatureRelativeToSurroundings
Indicates how an entity’s temperature compares to that of its immediate surroundings (e.g., warmer, cooler, or equal).
-
E.
temperatureOrderOfMagnitude
Indicates that the temperatures of the related entities differ by approximately a specified order of magnitude (i.e., by a power-of-ten scale factor).
- 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_69c6884f3db4819080ad65da69386206 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e0c74fe48190aeaa018631e52ef6 |
completed | March 27, 2026, 7:55 p.m. |
| PD | Predicate disambiguation | batch_69c6d7bd5a388190a57a96d925696ff6 |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:28 p.m.