Triple
T6514322
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baojun 310 |
E148214
|
entity |
| Predicate | primaryFuelEconomyFocus |
P23422
|
FINISHED |
| Object | city fuel efficiency |
—
|
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: city fuel efficiency | Statement: [Baojun 310, primaryFuelEconomyFocus, city fuel efficiency]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryFuelEconomyFocus Context triple: [Baojun 310, primaryFuelEconomyFocus, city fuel efficiency]
-
A.
fuelEfficiency
Indicates how effectively an entity uses fuel to perform a given amount of work or travel a certain distance.
-
B.
associatedWithFuelEconomy
chosen
Indicates a relationship where something is connected or relevant to fuel economy, such as influencing, measuring, or describing fuel efficiency.
-
C.
fuelConsumption
Indicates the amount of fuel used by an entity (such as a vehicle or device) over a specified distance, time, or operation.
-
D.
typicalFuel
Indicates the kind of fuel that is normally or most commonly used by an entity (such as a device, vehicle, or system).
-
E.
fuelRole
Indicates that one entity serves as the fuel or energy source used or consumed by another entity in a process or operation.
- 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_69c687e68e748190baceb9298f32d3ed |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6ac0bea808190aebc2905fb53eeba |
completed | March 27, 2026, 4:10 p.m. |
| PD | Predicate disambiguation | batch_69c68ab98c78819081743e614df04e1d |
completed | March 27, 2026, 1:48 p.m. |
Created at: March 27, 2026, 1:44 p.m.