Triple
T11376007
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Badshahi Mosque |
E269470
|
entity |
| Predicate | courtyardArea |
P48041
|
FINISHED |
| Object | approximately 276000 square feet |
—
|
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: approximately 276000 square feet | Statement: [Badshahi Mosque, courtyardArea, approximately 276000 square feet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: courtyardArea Context triple: [Badshahi Mosque, courtyardArea, approximately 276000 square feet]
-
A.
hasCourtyardArea
chosen
Indicates that an entity includes or is associated with a courtyard and specifies the size or extent of that courtyard space.
-
B.
courtyardCapacity
Indicates the maximum number of entities that can be accommodated in a courtyard at the same time.
-
C.
courtyardType
Indicates the specific kind or classification of a courtyard associated with an entity.
-
D.
courtyardShape
Indicates the geometric form or configuration that characterizes a courtyard.
-
E.
courtyardName
Indicates that an entity has a specific name assigned to a courtyard.
- 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_69d6aacca1048190b39dbbc2174616fa |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d800160a1c81909d115bf89fe54a49 |
completed | April 9, 2026, 7:37 p.m. |
| PD | Predicate disambiguation | batch_69d7e7022d508190996f9be0847c2b41 |
completed | April 9, 2026, 5:50 p.m. |
Created at: April 8, 2026, 9:33 p.m.