Triple
T2254979
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Surah Al-Qamar |
E49700
|
entity |
| Predicate | firstVerseTranslation |
P25592
|
FINISHED |
| Object | The Hour has drawn near, and the moon has split. |
—
|
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: The Hour has drawn near, and the moon has split. | Statement: [Surah Al-Qamar, firstVerseTranslation, The Hour has drawn near, and the moon has split.]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstVerseTranslation Context triple: [Surah Al-Qamar, firstVerseTranslation, The Hour has drawn near, and the moon has split.]
-
A.
firstLineTranslation
Indicates that one text is a translation of the first line of another text.
-
B.
refrainTranslation
Indicates that one expression is a translation of the repeated or recurring part (refrain) of another expression, typically in a different language.
-
C.
firstWord
Indicates that one entity is the first word in the sequence or text associated with another entity.
-
D.
verses
Indicates a relationship where one entity competes or is pitted against another, as in an opposition, matchup, or comparison.
-
E.
translationOfOpeningWords
chosen
Indicates that one text is a translation of the initial words or opening phrase of another text.
- 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_69a88aaa9250819095e127d0d77e8a32 |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc121af78819085b2e601d2f9bcdf |
completed | March 7, 2026, 6:09 a.m. |
| PD | Predicate disambiguation | batch_69abbdb34c148190b51e99f540f97204 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:47 p.m.