Triple
T12070103
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dulong–Petit law |
E287400
|
entity |
| Predicate | unitContext |
P103041
|
FINISHED |
| Object | joules per mole 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: joules per mole kelvin | Statement: [Dulong–Petit law, unitContext, joules per mole kelvin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: unitContext Context triple: [Dulong–Petit law, unitContext, joules per mole kelvin]
-
A.
contextType
Indicates the type or category of contextual information associated with an entity or event.
-
B.
canonicalContext
Indicates the standard or primary contextual framework within which an entity, statement, or resource is to be interpreted.
-
C.
contextOf
Indicates that one entity provides the situational, informational, or environmental background within which another entity exists, occurs, or is interpreted.
-
D.
systemContext
Indicates the contextual conditions, settings, or environment within which a system operates or an interaction occurs.
-
E.
unit
Indicates that two quantities are measured using the same standard unit of measurement.
- 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_69d6ab4846e081908ee7bbd66a6d3459 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9100b4ca8819084845ca4c13e34ce |
completed | April 10, 2026, 2:58 p.m. |
| PD | Predicate disambiguation | batch_69d902bda47c8190b94860b31df4a98c |
completed | April 10, 2026, 2:01 p.m. |
| PDg | Predicate description generation | batch_69d91006e14081909838412df082f794 |
completed | April 10, 2026, 2:58 p.m. |
Created at: April 8, 2026, 9:48 p.m.