Triple
T25017988
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bajrakli Mosque |
E626191
|
entity |
| Predicate | hasArabicInscription |
P161632
|
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: [Bajrakli Mosque, hasArabicInscription, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasArabicInscription Context triple: [Bajrakli Mosque, hasArabicInscription, yes]
-
A.
materialTypicallyInscribedOn
Indicates the material that is most commonly used as the surface or medium on which something is inscribed.
-
B.
oftenDepictedWithInscription
Indicates that the subject is frequently shown or represented together with a written inscription.
-
C.
EgyptianVersionInscribedOn
Indicates that an Egyptian-language version of a text or inscription is carved or written onto a particular physical object or surface.
-
D.
hasNumberOfNamesInscribed
Indicates the quantity of distinct names that are inscribed on a given entity.
-
E.
attestedInInscriptions
Indicates that evidence for the entity is documented or recorded in one or more inscriptions.
- 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_69e2ff27755881908490178e83701160 |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f6200ac60481909895c61d050b1338 |
completed | May 2, 2026, 4:02 p.m. |
| PD | Predicate disambiguation | batch_69f61b37a5648190b10d33ae205ccfee |
completed | May 2, 2026, 3:41 p.m. |
| PDg | Predicate description generation | batch_69f61f109ef48190873bfe18638d2046 |
completed | May 2, 2026, 3:58 p.m. |
Created at: April 18, 2026, 6:06 a.m.