Triple
T29515726
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Panorama Museum Bad Frankenhausen |
E748789
|
entity |
| Predicate | hasPanoramaArea |
P194700
|
FINISHED |
| Object | about 1722 square meters |
—
|
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: about 1722 square meters | Statement: [Panorama Museum Bad Frankenhausen, hasPanoramaArea, about 1722 square meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPanoramaArea Context triple: [Panorama Museum Bad Frankenhausen, hasPanoramaArea, about 1722 square meters]
-
A.
hasPanoramicView
Indicates that something offers a wide, unobstructed view over a broad surrounding area.
-
B.
hasPanControl
Indicates that an entity has the ability to control the horizontal movement (panning) of another entity, such as a camera or view.
-
C.
hasSummitPanorama
Indicates that a summit location offers a panoramic view or image captured from its highest point.
-
D.
hasCameraCoverage
Indicates that a specified area, object, or location is within the field of view or monitoring range of a particular camera or set of cameras.
-
E.
hasAperture
Indicates that one entity possesses or is characterized by a specific opening, gap, or aperture.
- 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_69f0bd461c208190bec20bbf24e02cc5 |
completed | April 28, 2026, 1:59 p.m. |
| NER | Named-entity recognition | batch_69fd82ed2a4c81908bd7797fbd2e3d08 |
completed | May 8, 2026, 6:30 a.m. |
| PD | Predicate disambiguation | batch_69fd814cc10481908e4f8123d35a5d0c |
completed | May 8, 2026, 6:23 a.m. |
| PDg | Predicate description generation | batch_69fd82ebe1c081908455fc45b6e45178 |
completed | May 8, 2026, 6:30 a.m. |
Created at: April 28, 2026, 4:37 p.m.