Triple
T13046314
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Presidential Administration Building |
E327331
|
entity |
| Predicate | hasInnerCourtyard |
P10828
|
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: [Presidential Administration Building, hasInnerCourtyard, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInnerCourtyard Context triple: [Presidential Administration Building, hasInnerCourtyard, yes]
-
A.
hasCourtyard
chosen
Indicates that one entity includes, features, or is characterized by the presence of a courtyard.
-
B.
hasCourtyardFeature
Indicates that a courtyard possesses or includes a specific feature or characteristic.
-
C.
hasCourtyardArea
Indicates that an entity includes or is associated with a courtyard and specifies the size or extent of that courtyard space.
-
D.
courtyardType
Indicates the specific kind or classification of a courtyard associated with an entity.
-
E.
numberOfMainCourtyards
Indicates the quantity of primary courtyards associated with an entity.
- 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_69d8076e64308190904fb5c93517c901 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d98a9829b48190b23624b6b3df4600 |
completed | April 10, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69d9803aca4c8190b1015cd159cc47a9 |
completed | April 10, 2026, 10:56 p.m. |
Created at: April 9, 2026, 8:56 p.m.