Triple
T12703529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clausius–Clapeyron relation |
E303520
|
entity |
| Predicate | hasUnitContext |
P103041
|
FINISHED |
| Object | SI units |
—
|
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: SI units | Statement: [Clausius–Clapeyron relation, hasUnitContext, SI units]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUnitContext Context triple: [Clausius–Clapeyron relation, hasUnitContext, SI units]
-
A.
hasUnitOf
Indicates that a quantity, measurement, or value is expressed in terms of a specific unit.
-
B.
unitContext
chosen
Indicates the contextual framework or setting (such as scope, conditions, or environment) within which a particular unit, instance, or occurrence is defined or interpreted.
-
C.
hasUnitConstant
Indicates that something is associated with a fixed, standard unit of measurement used to express its value.
-
D.
hasUnitStructure
Indicates that an entity possesses a specific internal organization or arrangement that defines its structural composition.
-
E.
hasBasedUnit
Indicates that something is defined or measured in terms of a specified base unit.
- 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_69d7bdef90d48190b46b88270e780946 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d962a32c6481908ddaddae4ea267bf |
completed | April 10, 2026, 8:50 p.m. |
| PD | Predicate disambiguation | batch_69d960be63f081908a5ef5ef17a311bf |
completed | April 10, 2026, 8:42 p.m. |
Created at: April 9, 2026, 5:23 p.m.