Triple
T1289357
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hanafi school |
E27508
|
entity |
| Predicate | languageOfClassicalTexts |
P6519
|
FINISHED |
| Object | Arabic |
—
|
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: Arabic | Statement: [Hanafi school, languageOfClassicalTexts, Arabic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfClassicalTexts Context triple: [Hanafi school, languageOfClassicalTexts, Arabic]
-
A.
mainClassicalLanguage
chosen
Indicates the primary classical (historically foundational) language associated with an entity, such as a text, culture, or scholarly tradition.
-
B.
literaryUnit
Indicates that one entity is a distinct segment or component (such as a chapter, scene, or passage) within a larger literary work or text.
-
C.
typicalLanguageOfReadings
Indicates the language that is most commonly used for readings or interpretations associated with a given entity.
-
D.
hasSacredText
Indicates that an entity possesses or is associated with a particular sacred or religious text.
-
E.
ancientLanguages
Indicates that the related entities are languages that originated in and were used during ancient historical periods.
- 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_69a496d4ec448190ad653b2590c46711 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c0d4dfb081908c8825d6062b1d99 |
completed | March 1, 2026, 10:42 p.m. |
| PD | Predicate disambiguation | batch_69a4bee41ca08190b0ad6f7ea40c0b62 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:51 p.m.