Triple
T15973100
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zuma |
E387371
|
entity |
| Predicate | vehicleCapability |
P67070
|
FINISHED |
| Object | amphibious |
—
|
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: amphibious | Statement: [Zuma, vehicleCapability, amphibious]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: vehicleCapability Context triple: [Zuma, vehicleCapability, amphibious]
-
A.
towingCapability
Indicates the maximum load or object weight that one entity is able to pull or tow.
-
B.
vehicleEligibility
Indicates whether a given vehicle satisfies the required conditions or criteria to be considered eligible for a specified purpose or program.
-
C.
hasVehicleFeature
chosen
Indicates that a vehicle possesses, includes, or is equipped with a specific feature or characteristic.
-
D.
cargoCapacityFeature
Indicates that an entity has a feature specifying how much cargo it can carry or accommodate.
-
E.
featuresVehicle
Indicates that one entity includes, presents, or prominently incorporates a particular vehicle as part of its content, composition, or offering.
- 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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e173b3bf6c81909230170e833d7ce7 |
completed | April 16, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69e142d6fb588190b4176eab4bbae774 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:54 a.m.