Triple
T14313454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | سورة التكاثر |
E354892
|
entity |
| Predicate | تبدأ بحرف |
P100500
|
FINISHED |
| Object | الهمزة في كلمة ألهاكم |
—
|
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: الهمزة في كلمة ألهاكم | Statement: [سورة التكاثر, تبدأ بحرف, الهمزة في كلمة ألهاكم]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: تبدأ بحرف Context triple: [سورة التكاثر, تبدأ بحرف, الهمزة في كلمة ألهاكم]
-
A.
beganWith
Indicates that one event, process, or state started with or was initiated by another specified event, process, or state.
-
B.
firstLetter
Indicates that one entity is the initial character or starting letter of another entity (typically a string or word).
-
C.
eachStanzaBeginsWithLetterOf
Indicates that every stanza in a text starts with a specific given letter.
-
D.
openingCharacter
chosen
Indicates that one entity is the first character or symbol at the beginning of another entity (such as a string, word, or text).
-
E.
began
Indicates that one entity initiated or started an action, event, or state involving another entity or context.
- 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_69d8278ed42c8190b9f882dcce611347 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de85b49e5481909b9ffab2d922e284 |
completed | April 14, 2026, 6:21 p.m. |
| PD | Predicate disambiguation | batch_69de2a9515f4819081aabf251bca5878 |
completed | April 14, 2026, 11:52 a.m. |
Created at: April 10, 2026, 1:12 a.m.