Triple
T1385449
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monsieur Ibrahim |
E29833
|
entity |
| Predicate | portraysEthnicityOrReligion |
P8262
|
FINISHED |
| Object | Turkish Muslim community |
—
|
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: Turkish Muslim community | Statement: [Monsieur Ibrahim, portraysEthnicityOrReligion, Turkish Muslim community]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysEthnicityOrReligion Context triple: [Monsieur Ibrahim, portraysEthnicityOrReligion, Turkish Muslim community]
-
A.
ethnoreligiousIdentity
chosen
Indicates a relationship where an entity is characterized by a combined ethnic and religious group identity.
-
B.
ethnicReligion
Indicates that a religion is closely associated with a particular ethnic group, often tied to that group’s culture, ancestry, or identity.
-
C.
depictsNationality
Indicates that one entity visually represents or portrays the nationality or national identity of another entity.
-
D.
religionMinority
Indicates that the subject’s religion is a minority faith within the relevant population or context.
-
E.
culturalDepictionBy
Indicates that one entity serves as the creator or source of a cultural representation or portrayal of another 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_69a498dc92f8819094a1108f8ac90f43 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c339f3d481909c04b14129899945 |
completed | March 1, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69a4befe343c81909f758440a531b5be |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:59 p.m.