Triple
T36672413
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montefiore Conca |
E905452
|
entity |
| Predicate | hasScenicViewOver |
P9193
|
FINISHED |
| Object | surrounding countryside |
—
|
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: surrounding countryside | Statement: [Montefiore Conca, hasScenicViewOver, surrounding countryside]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasScenicViewOver Context triple: [Montefiore Conca, hasScenicViewOver, surrounding countryside]
-
A.
hasScenicViewOf
chosen
Indicates that one entity offers a visually appealing or picturesque view of another entity.
-
B.
hasScenicViewpoints
Indicates that something includes or provides locations specifically intended for enjoying scenic or panoramic views.
-
C.
hasScenicAccessTo
Indicates that one place or object provides a visually appealing or notable view of another place or object.
-
D.
hasScenicPassNearby
Indicates that a location is situated close to a notable scenic pass, such as a mountain or landscape viewpoint route.
-
E.
isScenicViewpoint
Indicates that a location offers a notable, aesthetically pleasing view of the surrounding landscape.
- 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_69f76e6f10008190aea41746aa1b186e |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fd67191cf88190b53ecbf5be3564e9 |
completed | May 8, 2026, 4:31 a.m. |
| PD | Predicate disambiguation | batch_69fd654fdaac81908e67e75194710f06 |
completed | May 8, 2026, 4:23 a.m. |
Created at: May 3, 2026, 4:12 p.m.