Triple
T3270810
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BMW i8 |
E68641
|
entity |
| Predicate | CO2Emissions_g_km |
P31410
|
FINISHED |
| Object | approximately 42 |
—
|
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: approximately 42 | Statement: [BMW i8, CO2Emissions_g_km, approximately 42]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: CO2Emissions_g_km Context triple: [BMW i8, CO2Emissions_g_km, approximately 42]
-
A.
fuelEfficiency
Indicates how effectively an entity uses fuel to perform a given amount of work or travel a certain distance.
-
B.
carbonDioxideLevel
chosen
Indicates the measured concentration or amount of carbon dioxide present in a given environment or system.
-
C.
emissionsControl
Indicates a relationship where one entity regulates, limits, or manages the release of emissions produced by another entity or process.
-
D.
associatedWithFuelEconomy
Indicates a relationship where something is connected or relevant to fuel economy, such as influencing, measuring, or describing fuel efficiency.
-
E.
typeOfGasUsed
Indicates the specific kind of gas that is utilized in relation to an entity or process.
- 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_69ad859b54f881909bf530d549caf2fd |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adaff349148190beae8c0994b7ad83 |
completed | March 8, 2026, 5:20 p.m. |
| PD | Predicate disambiguation | batch_69ada41d7eac8190ada4bf5f793d5c49 |
completed | March 8, 2026, 4:30 p.m. |
Created at: March 8, 2026, 3:09 p.m.