Triple
T33051529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | طور |
E845735
|
entity |
| Predicate | quranicTermType |
P175747
|
FINISHED |
| Object | اسم مكان |
—
|
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: اسم مكان | Statement: [طور, quranicTermType, اسم مكان]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: quranicTermType Context triple: [طور, quranicTermType, اسم مكان]
-
A.
quranicChapterType
Indicates the classification relationship that specifies what type or category a given Quranic chapter belongs to.
-
B.
quranicOppositeTerm
Indicates that one term is presented in the Quran as the conceptual or semantic opposite of another term.
-
C.
quranicPhrase
Indicates that one entity is a phrase or expression that appears in, or is directly derived from, the Quran.
-
D.
hasQuranicEquivalent
Indicates that something has a corresponding or analogous concept, term, or passage found in the Quran.
-
E.
منهج_في_مفردات_ألفاظ_القرآن
Indicates a methodological relationship focused on analyzing and explaining the vocabulary and individual words of the Qur’an.
- F. None of above. chosen
Provenance (4 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_69f3495242e48190996a2cb2beab5455 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6d74b20a48190900dda1014cc13a8 |
completed | May 3, 2026, 5:04 a.m. |
| PD | Predicate disambiguation | batch_69f6d27120988190aacec621cf2bf0e8 |
completed | May 3, 2026, 4:43 a.m. |
| PDg | Predicate description generation | batch_69f6d6a482fc8190b526291cd99b8696 |
completed | May 3, 2026, 5:01 a.m. |
Created at: May 1, 2026, 1:24 a.m.