Triple
T18744964
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tawi-Tawi |
E458384
|
entity |
| Predicate | Sheik Karimul Makhdum MosqueInstanceOf |
P133387
|
FINISHED |
| Object | historic mosque |
—
|
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: historic mosque | Statement: [Tawi-Tawi, Sheik Karimul Makhdum MosqueInstanceOf, historic mosque]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: Sheik Karimul Makhdum MosqueInstanceOf Context triple: [Tawi-Tawi, Sheik Karimul Makhdum MosqueInstanceOf, historic mosque]
-
A.
JumaMosqueInstanceOf
Indicates that something is classified as an instance of a Juma (Friday) mosque.
-
B.
hasMosque
Indicates that one entity possesses, contains, or is the location of a mosque.
-
C.
mosqueNamedAfter
Indicates that a mosque bears the name of a particular person, place, event, or concept.
-
D.
isOneOfTheLargestMosquesIn
Indicates that the subject mosque ranks among the largest mosques located in the specified place or region.
-
E.
hasNumberOfMosques
Indicates the quantity of mosques associated with a given entity.
- 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_69d8d394dc308190b6725073f5db324c |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e57691c8688190b225cbd88493d9d1 |
completed | April 20, 2026, 12:42 a.m. |
| PD | Predicate disambiguation | batch_69e48d03766c8190a43f7681842f4f8d |
completed | April 19, 2026, 8:06 a.m. |
| PDg | Predicate description generation | batch_69e49a9bcc0c81908df3e513fd6762ff |
completed | April 19, 2026, 9:04 a.m. |
Created at: April 10, 2026, 11:51 a.m.