Triple
T15350969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | سورة تبت |
E367048
|
entity |
| Predicate | تُصنَّف من حيث الطول |
P88665
|
FINISHED |
| Object | من قصار السور |
—
|
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: من قصار السور | Statement: [سورة تبت, تُصنَّف من حيث الطول, من قصار السور]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: تُصنَّف من حيث الطول Context triple: [سورة تبت, تُصنَّف من حيث الطول, من قصار السور]
-
A.
lengthClass
chosen
Indicates a classification relationship where an entity is assigned to a category based on its length.
-
B.
typicalLength
Indicates the usual or characteristic length associated with an entity or phenomenon.
-
C.
lengthRegime
Indicates a specific range or category of length within which something operates, is measured, or is classified.
-
D.
trailLengthCategory
Indicates the classification of a trail based on its total length (e.g., short, medium, long).
-
E.
hasNameLengthCategory
Indicates that an entity is associated with a classification describing the length of its name (e.g., short, medium, long).
- 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_69d85a1355608190a6673ddb67231d54 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e290efc8190b22c95dcd3e5f57f |
completed | April 16, 2026, 1:40 a.m. |
| PD | Predicate disambiguation | batch_69deca991e5081908b0df3d1ee7d5338 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:17 a.m.