Triple
T37349497
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | الممتحنة |
E927282
|
entity |
| Predicate | لغة_النزول |
P104873
|
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: [الممتحنة, لغة_النزول, اللغة العربية]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: لغة_النزول Context triple: [الممتحنة, لغة_النزول, اللغة العربية]
-
A.
لغة السورة
chosen
Indicates the language in which a given surah (chapter of the Qur’an) is expressed or written.
-
B.
suffixLanguage
Indicates that one language is used as a suffix or ending element in the formation or representation of another language or linguistic expression.
-
C.
vernacularOf
Indicates that one language or dialect is the everyday, locally used form corresponding to another, more general or standard language.
-
D.
languageAtTerminus
Indicates the language used or associated with the endpoint or terminus of something (such as a route, connection, or communication).
-
E.
lexifierLanguage
Indicates that one language serves as the primary source or base language from which the core vocabulary and structure of another language, typically a pidgin or creole, are derived.
- 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_69f76eb5e034819088e53ab5b7909a68 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fb8c38a9688190be524246f5682107 |
completed | May 6, 2026, 6:45 p.m. |
| PD | Predicate disambiguation | batch_69fb5a9c6e0481908565bd849e869b24 |
completed | May 6, 2026, 3:13 p.m. |
Created at: May 3, 2026, 4:16 p.m.