Triple
T32838605
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | بحر المتقارب |
E839901
|
entity |
| Predicate | يُستخدم مثالاً في |
P41975
|
FINISHED |
| Object | كتب العَروض الكلاسيكية |
—
|
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: كتب العَروض الكلاسيكية | Statement: [بحر المتقارب, يُستخدم مثالاً في, كتب العَروض الكلاسيكية]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: يُستخدم مثالاً في Context triple: [بحر المتقارب, يُستخدم مثالاً في, كتب العَروض الكلاسيكية]
-
A.
usedAsExampleIn
chosen
Indicates that one entity is cited or presented as an illustrative example within another entity, such as a text, discussion, or explanation.
-
B.
isUsedToIllustrate
Indicates that one entity serves as an example or demonstration to clarify, explain, or represent another entity.
-
C.
hasExample
Indicates that one entity serves as an instance, illustration, or concrete example of another entity.
-
D.
sampleUsage
Indicates that an entity is used as an example or illustration to demonstrate how something works or is applied.
-
E.
teachingExample
Indicates that one entity serves as an illustrative or instructional example used to teach or clarify something to another entity.
- 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_69f3493ff0888190b51e974eae2a7834 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6d16f5cb881908eed141afaaa0b51 |
completed | May 3, 2026, 4:39 a.m. |
| PD | Predicate disambiguation | batch_69f6cfe45554819089cbbd538d992132 |
completed | May 3, 2026, 4:32 a.m. |
Created at: May 1, 2026, 1:16 a.m.