Triple
T35115156
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gran Turismo 4 |
E1013411
|
entity |
| Predicate | hasPhysicsEngine |
P69440
|
FINISHED |
| Object | realistic car handling model |
—
|
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: realistic car handling model | Statement: [Gran Turismo 4, hasPhysicsEngine, realistic car handling model]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPhysicsEngine Context triple: [Gran Turismo 4, hasPhysicsEngine, realistic car handling model]
-
A.
supportsPhysics
chosen
Indicates that one entity provides the necessary capabilities or features for another entity to handle, simulate, or work with physical behaviors or physics-based interactions.
-
B.
hasNoPhysicalBody
Indicates that an entity exists or operates without a tangible, material, or corporeal form.
-
C.
hasKinematicProperty
Indicates that an entity possesses a specific motion-related characteristic, such as velocity, acceleration, or trajectory.
-
D.
hasKinetics
Indicates that one entity is associated with the kinetic properties or rate-related behavior of another entity in a process or reaction.
-
E.
hasSoftBody
Indicates that an entity possesses a body that is flexible, yielding, or not rigid in structure.
- 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_69f76dd659d08190bcdc00d37caafb62 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f7c777e924819081a6634f549fe552 |
completed | May 3, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69f7c475c58c8190a883554231e88c88 |
completed | May 3, 2026, 9:56 p.m. |
Created at: May 3, 2026, 4:01 p.m.