Triple
T25861119
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | أبو الفرج ابن الجوزي |
E651478
|
entity |
| Predicate | تميّز بـ |
P662
|
FINISHED |
| Object | الأسلوب الوعظي المؤثر |
—
|
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: الأسلوب الوعظي المؤثر | Statement: [أبو الفرج ابن الجوزي, تميّز بـ, الأسلوب الوعظي المؤثر]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: تميّز بـ Context triple: [أبو الفرج ابن الجوزي, تميّز بـ, الأسلوب الوعظي المؤثر]
-
A.
يميز
Indicates that one entity distinguishes, differentiates, or sets something apart from something else.
-
B.
characterizedBy
chosen
Indicates that one entity possesses a defining quality, feature, or attribute expressed by another entity.
-
C.
distinguishingTrait
Indicates that a particular characteristic or feature uniquely differentiates one entity from another.
-
D.
benefitCharacteristic
Indicates that one entity possesses a quality or feature that provides an advantage, usefulness, or positive effect to another entity.
-
E.
contrastCharacteristic
Indicates that two entities are being compared by highlighting opposing or significantly different characteristics between them.
- 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_69e7ab3a199c81909227cb964cacfe24 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f6026b467c8190a1f8be336f8679da |
completed | May 2, 2026, 1:55 p.m. |
| PD | Predicate disambiguation | batch_69f4a0fed15881909b789251fe5d8d45 |
completed | May 1, 2026, 12:47 p.m. |
Created at: April 22, 2026, 8:05 a.m.