Triple
T1568202
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Riversdale House Museum |
E33479
|
entity |
| Predicate | hasAgriculturalLandscape |
P22129
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Riversdale House Museum, hasAgriculturalLandscape, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAgriculturalLandscape Context triple: [Riversdale House Museum, hasAgriculturalLandscape, yes]
-
A.
hasAgriculturalProduction
Indicates that an entity engages in or is characterized by the production of agricultural goods such as crops or livestock.
-
B.
hasLandscapeFeatures
chosen
Indicates that an entity possesses or includes specific landscape-related characteristics or elements.
-
C.
locatedInAgriculturalRegion
Indicates that an entity is situated within a region primarily characterized by agricultural activities or land use.
-
D.
hasRuralArea
Indicates that an entity includes, is associated with, or contains a countryside or sparsely populated geographic area.
-
E.
hasDiverseLandscape
Indicates that an entity possesses a variety of distinct physical or environmental features within its geographic area.
- 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_69a885f11b048190935025a035302715 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a90fccd4b48190a44012888a00af7f |
completed | March 5, 2026, 5:08 a.m. |
| PD | Predicate disambiguation | batch_69a907b872f0819096b3df6ad502c63e |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:27 p.m.