Triple
T12295838
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ultra Panavision 70 |
E293083
|
entity |
| Predicate | soundOnPrint |
P3514
|
FINISHED |
| Object | magnetic stripes on 70 mm print |
—
|
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: magnetic stripes on 70 mm print | Statement: [Ultra Panavision 70, soundOnPrint, magnetic stripes on 70 mm print]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: soundOnPrint Context triple: [Ultra Panavision 70, soundOnPrint, magnetic stripes on 70 mm print]
-
A.
soundEngine
Indicates that one entity functions as or provides the sound engine (audio processing or synthesis system) used by another entity.
-
B.
soundCharacter
Indicates a relationship where one entity specifies the quality, style, or distinguishing characteristics of a sound produced or perceived in another entity.
-
C.
sounderFunction
Indicates a functional relationship where one entity serves as or performs the role of a sounder (a device or mechanism that produces or detects sound) for another entity.
-
D.
soundReproductionMethod
chosen
Indicates the method or technique used to reproduce or play back sound.
-
E.
hasSound
Indicates that an entity produces, emits, or is associated with a particular sound.
- 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_69d6ab690ad081908c0ed3870ec82d53 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f621570819091ee1db2609233ea |
completed | April 10, 2026, 6:20 p.m. |
| PD | Predicate disambiguation | batch_69d93ec02c008190a56aae60a3d9eff6 |
completed | April 10, 2026, 6:17 p.m. |
Created at: April 8, 2026, 9:52 p.m.