Triple
T24307923
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | القدوس |
E612583
|
entity |
| Predicate | مرتبط لغوياً بـ |
P10003
|
FINISHED |
| Object | القداسة |
—
|
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: القداسة | Statement: [القدوس, مرتبط لغوياً بـ, القداسة]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: مرتبط لغوياً بـ Context triple: [القدوس, مرتبط لغوياً بـ, القداسة]
-
A.
linkedToLanguage
Indicates that an entity has an association or connection with a specific language, such as being expressed in, related to, or dependent on that language.
-
B.
linguisticallyRelatedTo
chosen
Indicates that two entities are connected through a linguistic relationship, such as sharing a common language, origin, structure, or other language-based association.
-
C.
hasRelatedLanguage
Indicates that one language is related to another through shared linguistic origins, features, or classification.
-
D.
hasLinguisticAffiliation
Indicates a relationship where an entity is associated with or belongs to a particular language or linguistic group.
-
E.
belongsToLinguisticRegion
Indicates that one linguistic entity (such as a language, dialect, or speech variety) is associated with or situated within a particular linguistic region or area.
- 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_69e2d7d91bb48190bc5377d17a85fb21 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f2922821248190a1b274f839251ddc |
completed | April 29, 2026, 11:20 p.m. |
| PD | Predicate disambiguation | batch_69f1c45c6ec081908401b69424428100 |
completed | April 29, 2026, 8:42 a.m. |
Created at: April 18, 2026, 1:31 a.m.