Triple
T34505641
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2.3L EcoBoost I4 (Mustang base engine) |
E885877
|
entity |
| Predicate | targetVehicleClass |
P109335
|
FINISHED |
| Object | sports car |
—
|
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: sports car | Statement: [2.3L EcoBoost I4 (Mustang base engine), targetVehicleClass, sports car]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetVehicleClass Context triple: [2.3L EcoBoost I4 (Mustang base engine), targetVehicleClass, sports car]
-
A.
mainVehicleClass
chosen
Indicates the primary category or type of vehicle to which an entity chiefly belongs.
-
B.
intendedVehicleClass
Indicates that one entity is designed or specified to be used with, or is appropriate for, a particular class or category of vehicle.
-
C.
vehicleClassInCTR
Indicates that a vehicle belongs to a specified vehicle class within a particular controlled traffic region (CTR).
-
D.
vehicleClassDriven
Indicates that an entity drives or operates a vehicle belonging to a particular vehicle class or category.
-
E.
vehicleType
Indicates the specific kind or category of vehicle associated with an entity (e.g., car, bus, bicycle).
- 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_69f349cc0220819081f154c6964f4dc2 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69feb8e856d48190aa34ad8ee8376e1c |
completed | May 9, 2026, 4:32 a.m. |
| PD | Predicate disambiguation | batch_69feb82a2b6c8190a473cc25976897be |
completed | May 9, 2026, 4:29 a.m. |
Created at: May 1, 2026, 2:01 a.m.