Triple
T6095124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | General Electric GEnx |
E135857
|
entity |
| Predicate | fuelEfficiencyComparedToPredecessor |
P41013
|
FINISHED |
| Object | improved |
—
|
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: improved | Statement: [General Electric GEnx, fuelEfficiencyComparedToPredecessor, improved]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fuelEfficiencyComparedToPredecessor Context triple: [General Electric GEnx, fuelEfficiencyComparedToPredecessor, improved]
-
A.
typicalEfficiencyComparedToPredecessor
chosen
Indicates how the usual or average efficiency of something compares to that of its predecessor.
-
B.
technologyLevelComparedToPredecessor
Indicates how the technology level of an entity compares to that of its immediate predecessor.
-
C.
fuelEfficiency
Indicates how effectively an entity uses fuel to perform a given amount of work or travel a certain distance.
-
D.
emissionsComparedToDiesel
Indicates how the emissions produced by something compare in amount or impact to those produced by diesel.
-
E.
winnerPowertrainType
Indicates the type of powertrain used by the entity that is identified as the winner in a given context or competition.
- 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_69c0087cd3c48190b459848c72d84eb1 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c05a963bac8190bc0c33fef187875c |
completed | March 22, 2026, 9:09 p.m. |
| PD | Predicate disambiguation | batch_69c049f5ac988190b62ba565153aaa35 |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:12 p.m.