Triple
T12295823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ultra Panavision 70 |
E293083
|
entity |
| Predicate | imageCharacteristics |
P25983
|
FINISHED |
| Object | extremely wide field of view |
—
|
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: extremely wide field of view | Statement: [Ultra Panavision 70, imageCharacteristics, extremely wide field of view]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: imageCharacteristics Context triple: [Ultra Panavision 70, imageCharacteristics, extremely wide field of view]
-
A.
visualFeature
chosen
Indicates a relationship where one entity possesses or exhibits a particular visual characteristic or attribute of another entity.
-
B.
mediaCharacterization
Indicates how an entity is portrayed, described, or framed by media sources in terms of attributes, tone, or narrative.
-
C.
textureFeatures
Indicates that one entity possesses or is characterized by specific surface or material texture properties described by the other entity.
-
D.
ruleCharacteristics
Indicates the defining properties, constraints, or parameters that specify how a particular rule operates or should be applied.
-
E.
dataCharacteristic
Indicates that one entity specifies a property, attribute, or feature that characterizes a given piece of data.
- 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.