Triple
T29515724
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Panorama Museum Bad Frankenhausen |
E748789
|
entity |
| Predicate | hasPanoramaDiameter |
P7302
|
FINISHED |
| Object | about 25 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 25 meters | Statement: [Panorama Museum Bad Frankenhausen, hasPanoramaDiameter, about 25 meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPanoramaDiameter Context triple: [Panorama Museum Bad Frankenhausen, hasPanoramaDiameter, about 25 meters]
-
A.
hasPanoramicView
Indicates that something offers a wide, unobstructed view over a broad surrounding area.
-
B.
approximateDiameter
chosen
Indicates that one entity specifies the estimated or rough measurement of another entity’s diameter.
-
C.
bodyDiameter
Indicates the measurement of how wide an object's body is across its broadest cross-section.
-
D.
hasPolarDiameter_km
Indicates the length of an object's diameter measured from pole to pole, expressed in kilometers.
-
E.
hasRoundWindowDiameter
Indicates that an entity possesses a round window whose size is specified by its diameter.
- 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_69f0bd461c208190bec20bbf24e02cc5 |
completed | April 28, 2026, 1:59 p.m. |
| NER | Named-entity recognition | batch_69fd7e364a648190a1e9e1d9fc76e99e |
completed | May 8, 2026, 6:09 a.m. |
| PD | Predicate disambiguation | batch_69fd7bb547608190a3b04dddbca6b8bc |
completed | May 8, 2026, 5:59 a.m. |
Created at: April 28, 2026, 4:37 p.m.