Triple
T24776
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Auburn Cemetery |
E493
|
entity |
| Predicate | landscapeStyle |
P1479
|
FINISHED |
| Object | picturesque |
—
|
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: picturesque | Statement: [Mount Auburn Cemetery, landscapeStyle, picturesque]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: landscapeStyle Context triple: [Mount Auburn Cemetery, landscapeStyle, picturesque]
-
A.
architecturalStyle
Indicates the architectural design tradition, movement, or style that characterizes the form and appearance of a structure or built work.
-
B.
vegetation
Indicates that an area or object is covered with, contains, or is characterized by plant life.
-
C.
vegetationType
Indicates the specific kind or category of plant cover or flora that characterizes a given area or environment.
-
D.
hasLandform
Indicates that one entity possesses, contains, or is characterized by a particular natural landform.
-
E.
hasNaturalFeature
Indicates that one entity possesses, contains, or is characterized by a particular natural feature (such as a mountain, river, forest, or coastline).
- 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_69a243b4ac2c8190b93c303df797b7b2 |
completed | Feb. 28, 2026, 1:24 a.m. |
| NER | Named-entity recognition | batch_69a246d794448190bb2844fcd0538eaa |
completed | Feb. 28, 2026, 1:37 a.m. |
| PD | Predicate disambiguation | batch_69a24657635881908f3415bc1bdfa1b5 |
completed | Feb. 28, 2026, 1:35 a.m. |
| PDg | Predicate description generation | batch_69a246d6aca88190a86b7c41d497bacd |
completed | Feb. 28, 2026, 1:37 a.m. |
Created at: Feb. 28, 2026, 1:34 a.m.