Triple
T4875300
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aziziye Mosque |
E109187
|
entity |
| Predicate | minaretStyle |
P53930
|
FINISHED |
| Object | slender Ottoman minarets |
—
|
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: slender Ottoman minarets | Statement: [Aziziye Mosque, minaretStyle, slender Ottoman minarets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: minaretStyle Context triple: [Aziziye Mosque, minaretStyle, slender Ottoman minarets]
-
A.
minaretShape
chosen
Indicates that one entity has the specified architectural shape or form of a minaret in relation to another entity.
-
B.
minaretMaterial
Indicates the material from which a minaret is constructed or primarily made.
-
C.
hasMinarets
Indicates that an entity (typically a building) possesses one or more minarets as architectural features.
-
D.
hasDomeAndMinarets
Indicates that something possesses both a dome and one or more minarets as architectural features.
-
E.
numberOfBalconiesOnMinarets
Indicates the count of balconies present on the minarets associated with a given subject.
- 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_69bd440e9d64819083e82cf33b4d9570 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6ff981fc819080d4466c6fe06cf3 |
completed | March 20, 2026, 4:04 p.m. |
| PD | Predicate disambiguation | batch_69bd6c28e56081908ee411ac94c3769e |
completed | March 20, 2026, 3:47 p.m. |
Created at: March 20, 2026, 1:27 p.m.