Triple
T26651284
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Āyat al-Taṭhīr |
E669064
|
entity |
| Predicate | textSegment |
P9883
|
FINISHED |
| Object | “innamā yurīdu llāhu li-yudhhiba ʿankumu r-rijsa ahla l-bayti wa yuṭahhirakum taṭhīrā” |
—
|
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: “innamā yurīdu llāhu li-yudhhiba ʿankumu r-rijsa ahla l-bayti wa yuṭahhirakum taṭhīrā” | Statement: [Āyat al-Taṭhīr, textSegment, “innamā yurīdu llāhu li-yudhhiba ʿankumu r-rijsa ahla l-bayti wa yuṭahhirakum taṭhīrā”]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: textSegment Context triple: [Āyat al-Taṭhīr, textSegment, “innamā yurīdu llāhu li-yudhhiba ʿankumu r-rijsa ahla l-bayti wa yuṭahhirakum taṭhīrā”]
-
A.
textFragment
chosen
Indicates that one piece of text is a constituent part or segment of a larger text.
-
B.
textContent
Indicates that one entity is the textual content or written material contained within another entity.
-
C.
textualDivisionOf
Indicates that one text segment functions as a structural subdivision (such as a chapter, section, or paragraph) within another text.
-
D.
textScript
Indicates the writing system or script in which a given piece of text is expressed.
-
E.
textType
Indicates the classification of a text according to its type, format, or genre.
- 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_69ee9d00eb5481908d6c6d0ada2f0c9a |
completed | April 26, 2026, 11:17 p.m. |
| NER | Named-entity recognition | batch_69f616798e408190b271a85ebdb78cd1 |
completed | May 2, 2026, 3:21 p.m. |
| PD | Predicate disambiguation | batch_69f60b8bb0d08190ab5a9a2a8847c6f4 |
completed | May 2, 2026, 2:34 p.m. |
Created at: April 27, 2026, 2:33 a.m.