Triple
T7446895
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Green Line |
E171905
|
entity |
| Predicate | efficiencyFocus |
P49749
|
FINISHED |
| Object | higher miles per gallon compared to non-hybrid Aura trims |
—
|
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: higher miles per gallon compared to non-hybrid Aura trims | Statement: [Green Line, efficiencyFocus, higher miles per gallon compared to non-hybrid Aura trims]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: efficiencyFocus Context triple: [Green Line, efficiencyFocus, higher miles per gallon compared to non-hybrid Aura trims]
-
A.
performanceFocus
Indicates that an entity directs attention or effort toward improving or emphasizing performance in a given context.
-
B.
maximumEfficiency
chosen
Indicates that an entity operates at its highest possible level of performance or productivity under given conditions.
-
C.
productivityLevel
Indicates the degree or intensity of how much useful output or work an entity produces over a given period or context.
-
D.
focusesOnWork
Indicates that an entity directs its attention, effort, or primary activity toward work-related tasks or responsibilities.
-
E.
optimize
Indicates improving a process, system, or outcome to achieve the best possible performance or efficiency under given constraints.
- 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_69c68a65402881908f7869368eb746fb |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f371be2081908feaeb9392cb65fe |
completed | March 27, 2026, 9:15 p.m. |
| PD | Predicate disambiguation | batch_69c6f039f7248190bb4183f97b605763 |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:14 p.m.