Triple
T7202451
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ayat al-Kursi |
E148579
|
entity |
| Predicate | lengthInArabicWordsApprox |
P75788
|
FINISHED |
| Object | 50 |
—
|
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: 50 | Statement: [Ayat al-Kursi, lengthInArabicWordsApprox, 50]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lengthInArabicWordsApprox Context triple: [Ayat al-Kursi, lengthInArabicWordsApprox, 50]
-
A.
lengthInWords
Indicates the number of words that make up the length of something, typically a text or expression.
-
B.
approximateLengthInMeters
Indicates the estimated or roughly measured length of something expressed in meters.
-
C.
length
Indicates a measurement relationship where a value specifies how long something is from one end to the other.
-
D.
hasNameInArabic
Indicates that an entity is associated with a specific name expressed in the Arabic language.
-
E.
hasApproximateNumberOfLetters
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
- 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_69c687e8cf188190b5f3ecffd681f04e |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6e94a9ee4819086de79fcdfa1836a |
completed | March 27, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69c6e757fed4819091b0a096e3befc3a |
completed | March 27, 2026, 8:23 p.m. |
| PDg | Predicate description generation | batch_69c6e8fd9b848190b2b1beea5698422b |
completed | March 27, 2026, 8:30 p.m. |
Created at: March 27, 2026, 2:52 p.m.