Triple
T35636253
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mitsubishi i |
E1029727
|
entity |
| Predicate | fuelEfficiencyFocus |
P23802
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Mitsubishi i, fuelEfficiencyFocus, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fuelEfficiencyFocus Context triple: [Mitsubishi i, fuelEfficiencyFocus, yes]
-
A.
fuelEfficiency
chosen
Indicates how effectively an entity uses fuel to perform a given amount of work or travel a certain distance.
-
B.
fuelConsumption
Indicates the amount of fuel used by an entity (such as a vehicle or device) over a specified distance, time, or operation.
-
C.
associatedWithFuelEconomy
Indicates a relationship where something is connected or relevant to fuel economy, such as influencing, measuring, or describing fuel efficiency.
-
D.
fuelEffect
Indicates the influence or impact that a given fuel has on a process, system, or outcome.
-
E.
typicalFuel
Indicates the kind of fuel that is normally or most commonly used by an entity (such as a device, vehicle, or system).
- 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_69f76e087bdc8190a4794bf9c0bd7634 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f79f48acec8190a9d5964581a94f6c |
completed | May 3, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_69f79e4d885881908a3612e2e75cf84f |
completed | May 3, 2026, 7:13 p.m. |
Created at: May 3, 2026, 4:05 p.m.