Triple
T10335493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harpton Court |
E242991
|
entity |
| Predicate | hasGardenOrPark |
P45219
|
FINISHED |
| Object | landscaped grounds |
—
|
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: landscaped grounds | Statement: [Harpton Court, hasGardenOrPark, landscaped grounds]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGardenOrPark Context triple: [Harpton Court, hasGardenOrPark, landscaped grounds]
-
A.
hasGardenSquare
Indicates that an entity includes, is adjacent to, or is otherwise associated with a garden square.
-
B.
hasNearbyGreenSpace
Indicates that an entity is located close to an area of green space, such as a park, garden, or natural vegetation.
-
C.
hasParkAndGardenRegister
Indicates that an entity is recorded in an official register of parks and gardens.
-
D.
containsGarden
Indicates that one entity includes or has a garden within its area or boundaries.
-
E.
hasGreenSpaces
chosen
Indicates that an entity includes or is associated with areas of vegetation or natural greenery, such as parks, gardens, or lawns.
- 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_69d381af787481908bc401325c760a88 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e91fdb2081909866c6ecf417d75a |
completed | April 7, 2026, 11:23 a.m. |
| PD | Predicate disambiguation | batch_69d4df9dc3208190bf1bd106f44f6202 |
completed | April 7, 2026, 10:42 a.m. |
Created at: April 6, 2026, 11:53 a.m.