Triple
T1108819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tin Lizzie |
E25545
|
entity |
| Predicate | associatedWithVehicleLayout |
P5253
|
FINISHED |
| Object | front-engine, rear-wheel-drive layout |
—
|
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: front-engine, rear-wheel-drive layout | Statement: [Tin Lizzie, associatedWithVehicleLayout, front-engine, rear-wheel-drive layout]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithVehicleLayout Context triple: [Tin Lizzie, associatedWithVehicleLayout, front-engine, rear-wheel-drive layout]
-
A.
vehicleLayout
chosen
Indicates how the components or seating within a vehicle are arranged or configured relative to each other.
-
B.
relatedVehicle
Indicates that there exists an associated or connected vehicle that has a relevant relationship to the primary entity.
-
C.
associatedVehicleWeightClass
Indicates the weight classification category that is linked or assigned to a particular vehicle.
-
D.
ownershipModel
Indicates the type or structure of ownership relationship that governs how control, rights, or shares are held between entities.
-
E.
hasVehicle
Indicates that one entity possesses, owns, or is assigned a vehicle.
- 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_69a49428d4448190b3b36991ceae87ce |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4b9e6134481909f348986a25f65c6 |
completed | March 1, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69a4b749e2a881909ef28745a7d2d917 |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:43 p.m.