Triple
T11420079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dyna |
E270594
|
entity |
| Predicate | hasFrameMounting |
P34295
|
FINISHED |
| Object | rubber-mounted engine on many models |
—
|
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: rubber-mounted engine on many models | Statement: [Dyna, hasFrameMounting, rubber-mounted engine on many models]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFrameMounting Context triple: [Dyna, hasFrameMounting, rubber-mounted engine on many models]
-
A.
hasMountingFeature
chosen
Indicates that one entity includes or provides a structural feature intended for mounting or attaching another entity.
-
B.
hasMount
Indicates that an entity is equipped with, riding, or otherwise using another entity as a mount for transportation or support.
-
C.
hasFramingDevice
Indicates that one entity serves as a narrative or structural framing device that contextualizes, introduces, or encloses the main content of another entity.
-
D.
hasFrameType
Indicates that an entity possesses or is associated with a specific type or category of frame.
-
E.
hasMapframe
Indicates that an entity is associated with an embedded, interactive map frame representation of its geographic location or area.
- 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_69d6aaddeaa8819088b30ef7b50598c9 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d801b20ce08190befc98379b879985 |
completed | April 9, 2026, 7:44 p.m. |
| PD | Predicate disambiguation | batch_69d7e71436f88190ac7e45a04ea5c987 |
completed | April 9, 2026, 5:51 p.m. |
Created at: April 8, 2026, 9:34 p.m.