Triple
T25735186
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montagnana |
E645356
|
entity |
| Predicate | hasPanoramicViewpoint |
P29603
|
FINISHED |
| Object | city walls and towers |
—
|
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: city walls and towers | Statement: [Montagnana, hasPanoramicViewpoint, city walls and towers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPanoramicViewpoint Context triple: [Montagnana, hasPanoramicViewpoint, city walls and towers]
-
A.
hasPanoramicView
chosen
Indicates that something offers a wide, unobstructed view over a broad surrounding area.
-
B.
hasViewingPointFor
Indicates that one entity serves as a vantage point or location from which another entity can be viewed or observed.
-
C.
hasSummitPanorama
Indicates that a summit location offers a panoramic view or image captured from its highest point.
-
D.
hasViewingAngles
Indicates that one entity possesses or is characterized by specific viewing angles relative to another entity or reference frame.
-
E.
hasFieldOfView
Indicates that one entity possesses a visual coverage area within which it can perceive or detect other entities or regions.
- 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_69e77e85254081908d79ee4e8715f283 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f68f670b608190a0b6ab60d722b4e0 |
completed | May 2, 2026, 11:57 p.m. |
| PD | Predicate disambiguation | batch_69f68b78f29481908cc8f390496dee97 |
completed | May 2, 2026, 11:40 p.m. |
Created at: April 21, 2026, 11:24 p.m.