Triple
T15899720
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Quran 3:18 |
E385550
|
entity |
| Predicate | numberOfWordsApproximate |
P113736
|
FINISHED |
| Object | about 20 in Arabic (depending on orthography) |
—
|
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: about 20 in Arabic (depending on orthography) | Statement: [Quran 3:18, numberOfWordsApproximate, about 20 in Arabic (depending on orthography)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfWordsApproximate Context triple: [Quran 3:18, numberOfWordsApproximate, about 20 in Arabic (depending on orthography)]
-
A.
hasApproximateNumberOfLetters
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
-
B.
approximateNumberOfVerses
Indicates an estimated or approximate count of verses associated with an entity.
-
C.
wordCount
Indicates the total number of words contained in a given text or linguistic unit.
-
D.
عدد كلماتها التقريبي
chosen
Indicates an approximate count of the number of words associated with an entity.
-
E.
lengthInWords
Indicates the number of words that make up the length of something, typically a text or expression.
- 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_69d86da5b800819083a31be937d738b0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e17d4d08f481909f38b75e3f42d9ab |
completed | April 17, 2026, 12:22 a.m. |
| PD | Predicate disambiguation | batch_69e142ca3b208190946c3aa4c1e6087c |
completed | April 16, 2026, 8:12 p.m. |
Created at: April 10, 2026, 4:51 a.m.