Triple
T26410656
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Panorama des Champs-Élysées |
E663949
|
entity |
| Predicate | visualExperience |
P160737
|
FINISHED |
| Object | immersive 360-degree viewing |
—
|
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: immersive 360-degree viewing | Statement: [Panorama des Champs-Élysées, visualExperience, immersive 360-degree viewing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: visualExperience Context triple: [Panorama des Champs-Élysées, visualExperience, immersive 360-degree viewing]
-
A.
visualDetail
Indicates that one entity provides or specifies the visual characteristics, features, or appearance details of another entity.
-
B.
vision
Indicates that an entity perceives another entity or object visually, using sight.
-
C.
viewOnReality
Indicates a subject’s overarching perspective, interpretation, or conceptual stance regarding the nature of reality.
-
D.
visualEffect
Indicates that one entity produces, modifies, or is associated with a particular visual effect on another entity or within a scene.
-
E.
visualCompanion
Indicates that one entity serves as a visual counterpart, partner, or accompanying element to another in a visual context.
- 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_69ee883931888190901be96d75ee23cc |
completed | April 26, 2026, 9:48 p.m. |
| NER | Named-entity recognition | batch_69f6113143f481909c64dfc1975e3a59 |
completed | May 2, 2026, 2:58 p.m. |
| PD | Predicate disambiguation | batch_69f602d5c8808190a1fdbebd6f0981e8 |
completed | May 2, 2026, 1:57 p.m. |
| PDg | Predicate description generation | batch_69f604120e848190b516c29b781d19cc |
completed | May 2, 2026, 2:02 p.m. |
Created at: April 26, 2026, 11:37 p.m.