Triple
T37074919
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carnot engine |
E917683
|
entity |
| Predicate | efficiencyFormula |
P187315
|
FINISHED |
| Object | 1 - Tc/Th |
—
|
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: 1 - Tc/Th | Statement: [Carnot engine, efficiencyFormula, 1 - Tc/Th]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: efficiencyFormula Context triple: [Carnot engine, efficiencyFormula, 1 - Tc/Th]
-
A.
efficiency
Indicates how effectively an entity converts inputs (such as time, energy, or resources) into desired outputs or results.
-
B.
sampleEfficiency
Indicates how effectively a method or system learns or performs using a limited number of samples or data points.
-
C.
speedupFormula
Indicates a quantitative relationship expressing how much faster one process or system becomes relative to another, typically as a ratio or factor of performance improvement.
-
D.
maximumEfficiency
Indicates that an entity operates at its highest possible level of performance or productivity under given conditions.
-
E.
netEfficiency
Indicates the overall effectiveness of a system or process after accounting for all losses, typically expressed as the ratio of useful output to total input.
- 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_69f76e9771e08190a690834e3cd20654 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb34e5576881909394355c8ec6ddd2 |
completed | May 6, 2026, 12:32 p.m. |
| PD | Predicate disambiguation | batch_69fb2f6171e88190bf1e0ee6a644b6a9 |
completed | May 6, 2026, 12:09 p.m. |
| PDg | Predicate description generation | batch_69fb34e4906c8190abb1c293fb84329a |
completed | May 6, 2026, 12:32 p.m. |
Created at: May 3, 2026, 4:14 p.m.