Triple
T34108190
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Singleton Park |
E874764
|
entity |
| Predicate | scenicSettingFor |
P20834
|
FINISHED |
| Object | Swansea University Singleton Park Campus |
—
|
NE NERFINISHED |
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: Swansea University Singleton Park Campus | Statement: [Singleton Park, scenicSettingFor, Swansea University Singleton Park Campus]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: scenicSettingFor Context triple: [Singleton Park, scenicSettingFor, Swansea University Singleton Park Campus]
-
A.
scenicDescription
Indicates a descriptive portrayal of the visual or aesthetic qualities of a scene or landscape.
-
B.
scenicCategory
Indicates the classification of a place or route based on its visual appeal or scenic qualities.
-
C.
isPartOfScenicVista
Indicates that something is included within, or contributes to, a larger scenic vista or panoramic view.
-
D.
placeOfSetting
chosen
Indicates the location or environment where an event, scene, or situation takes place.
-
E.
isScenicStartingPointFor
Indicates that a location serves as an especially picturesque or visually appealing starting point for a route, journey, or activity.
- 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_69f349a80d4481908527317d43f5c579 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f70caf57ec8190b0a453cc75eaa8f2 |
completed | May 3, 2026, 8:51 a.m. |
| PD | Predicate disambiguation | batch_69f70ac0170c819098e3b8e41d02efef |
completed | May 3, 2026, 8:43 a.m. |
Created at: May 1, 2026, 1:53 a.m.