Triple
T1462827
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wien displacement law |
E31550
|
entity |
| Predicate | hasSIUnitForConstant |
P28826
|
FINISHED |
| Object | meter kelvin |
—
|
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: meter kelvin | Statement: [Wien displacement law, hasSIUnitForConstant, meter kelvin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSIUnitForConstant Context triple: [Wien displacement law, hasSIUnitForConstant, meter kelvin]
-
A.
SIUnit
Indicates that something is expressed or measured using a unit from the International System of Units (SI).
-
B.
SIUnitSymbol
Indicates the standardized symbol used to represent a quantity in the International System of Units (SI).
-
C.
SIName
Indicates that an entity has a specific system or standardized identifier name associated with it.
-
D.
hasCODATAValue
Indicates that an entity is associated with a value specified or standardized by CODATA (Committee on Data for Science and Technology).
-
E.
standardUnitRelation
Indicates a relationship where one unit is defined, measured, or interpreted in terms of a recognized standard unit.
- 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_69a49917dfc081909acdbdf5d684f1ef |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c5b6e36c81909c47b2f7e66f17d7 |
completed | March 1, 2026, 11:03 p.m. |
| PD | Predicate disambiguation | batch_69a4c48121e48190946c23c583e5fb64 |
completed | March 1, 2026, 10:58 p.m. |
| PDg | Predicate description generation | batch_69a4c55508948190922aee3230a4323e |
completed | March 1, 2026, 11:01 p.m. |
Created at: March 1, 2026, 8 p.m.