Triple
T36425885
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Grim Game |
E897301
|
entity |
| Predicate | featuresStuntType |
P204869
|
FINISHED |
| Object | airplane-to-airplane transfer |
—
|
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: airplane-to-airplane transfer | Statement: [The Grim Game, featuresStuntType, airplane-to-airplane transfer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresStuntType Context triple: [The Grim Game, featuresStuntType, airplane-to-airplane transfer]
-
A.
featuresStuntShow
Indicates that something includes or presents a stunt show as part of its offerings or content.
-
B.
hasStunts
Indicates that one entity performs, includes, or is associated with stunt actions for another entity or context.
-
C.
offRoadFeature
Indicates that an entity possesses a characteristic, component, or capability specifically designed for off-road use or performance.
-
D.
featuresVehicle
Indicates that one entity includes, presents, or prominently incorporates a particular vehicle as part of its content, composition, or offering.
-
E.
stuntPerformerIn
Indicates that one entity serves as a stunt performer in a work, production, or performance associated with another entity.
- F. None of above. chosen
Provenance (4 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_69f76e559b10819099d6655a6e14587c |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0a54cc8190868c1bfa1590d1a6 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82f8c88190bd77a086023ac0e1 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:10 p.m.