Triple
T10846937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montpelier (Henry Knox Mansion) |
E256036
|
entity |
| Predicate | hasGardenOrGrounds |
P13651
|
FINISHED |
| Object | historic estate 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: historic estate grounds | Statement: [Montpelier (Henry Knox Mansion), hasGardenOrGrounds, historic estate grounds]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGardenOrGrounds Context triple: [Montpelier (Henry Knox Mansion), hasGardenOrGrounds, historic estate grounds]
-
A.
containsGarden
Indicates that one entity includes or has a garden within its area or boundaries.
-
B.
hasGardenType
Indicates that an entity possesses or is associated with a garden of a specified type.
-
C.
hasGardenSquare
Indicates that an entity includes, is adjacent to, or is otherwise associated with a garden square.
-
D.
gardenAccess
Indicates that one entity is permitted to enter or use a particular garden area.
-
E.
hasLandscaping
chosen
Indicates that an entity possesses or is associated with designed outdoor grounds or landscape features.
- 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_69d6aa81a5d08190aa86689061d1ddd2 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d75113bc188190ac78df0c51d95de6 |
completed | April 9, 2026, 7:11 a.m. |
| PD | Predicate disambiguation | batch_69d70d2b51448190bae748ed6c23edde |
completed | April 9, 2026, 2:21 a.m. |
Created at: April 8, 2026, 9:20 p.m.