Triple
T22658134
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | كمال الشيخ |
E559285
|
entity |
| Predicate | اللغة المستخدمة في الأعمال |
P125070
|
FINISHED |
| Object | اللغة العربية |
—
|
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: اللغة العربية | Statement: [كمال الشيخ, اللغة المستخدمة في الأعمال, اللغة العربية]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: اللغة المستخدمة في الأعمال Context triple: [كمال الشيخ, اللغة المستخدمة في الأعمال, اللغة العربية]
-
A.
لغة_الأعمال
Indicates a relationship where something is expressed, conducted, or communicated using the language of business (لغة الأعمال).
-
B.
languageWithinWork
chosen
Indicates that a specific language is used or contained within a particular work (such as a document, publication, or creative piece).
-
C.
languageOfUnderlyingWork
Indicates the language in which the original or underlying work (from which a derived or related work stems) is expressed.
-
D.
usedInLanguage
Indicates that something (such as a word, expression, or symbol) is employed or occurs within a particular language.
-
E.
primaryLanguageInWork
Indicates that a specified language is the main or predominant language used within a particular work (such as a book, film, or document).
- 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_69e245489dd88190b1f674acf61c8769 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1765d10588190b4574f3e64617cd4 |
completed | April 29, 2026, 3:09 a.m. |
| PD | Predicate disambiguation | batch_69ee6294c4c08190b7e4829f4b9af24b |
completed | April 26, 2026, 7:08 p.m. |
Created at: April 17, 2026, 3:07 p.m.