Triple
T1158118
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Al-Azhar Mosque |
E24429
|
entity |
| Predicate | regionInfluence |
P19397
|
FINISHED |
| Object | Muslim world |
—
|
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: Muslim world | Statement: [Al-Azhar Mosque, regionInfluence, Muslim world]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionInfluence Context triple: [Al-Azhar Mosque, regionInfluence, Muslim world]
-
A.
influencesRegion
chosen
Indicates that one entity has an effect on, shapes, or alters the conditions, characteristics, or behavior of a specified region.
-
B.
regionRuled
Indicates that a particular region is governed or controlled by a specified ruler or authority.
-
C.
regionException
Indicates an exception or exclusion to a rule, condition, or classification that applies specifically to a certain region or set of regions.
-
D.
regionType
Indicates the classification or category of a region, specifying what kind of region it is (e.g., administrative, geographic, or functional).
-
E.
cityOfInfluence
Indicates the city that significantly shapes, impacts, or exerts influence over a given 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_69a494060e148190abb42f971242c197 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bcab3cd08190ad06ea007042a8fc |
completed | March 1, 2026, 10:24 p.m. |
| PD | Predicate disambiguation | batch_69a4bb525b648190adcb7a29256d3c41 |
completed | March 1, 2026, 10:18 p.m. |
Created at: March 1, 2026, 7:45 p.m.