Triple
T22328261
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stoney Middleton |
E551957
|
entity |
| Predicate | hasLandscapeUseHistory |
P147752
|
FINISHED |
| Object | mining |
—
|
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: mining | Statement: [Stoney Middleton, hasLandscapeUseHistory, mining]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLandscapeUseHistory Context triple: [Stoney Middleton, hasLandscapeUseHistory, mining]
-
A.
hasLandscapeUse
Indicates that something is used or intended to be used within a landscape or landscaping context.
-
B.
hasLandscapeType
Indicates that an entity possesses or is characterized by a particular type or category of landscape.
-
C.
hasLandscapeProtection
Indicates that an area or object is subject to legal or formal measures aimed at preserving its landscape or scenic character.
-
D.
hasLandscapeFeatures
Indicates that an entity possesses or includes specific landscape-related characteristics or elements.
-
E.
hasHistoricalUsageIn
Indicates that something has been used or practiced within a particular historical period, context, or tradition.
- 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_69e11e482f788190b78d1588fc26d606 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15769fdb48190b84e0c019ab63579 |
completed | April 29, 2026, 12:57 a.m. |
| PD | Predicate disambiguation | batch_69e73004d9e88190bb862319a5aea06b |
completed | April 21, 2026, 8:06 a.m. |
| PDg | Predicate description generation | batch_69e7342ce08c8190bc0a7085f4a952e7 |
completed | April 21, 2026, 8:24 a.m. |
Created at: April 16, 2026, 8:42 p.m.