Triple
T9461474
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | سيحون |
E228154
|
entity |
| Predicate | اللغة_التقليدية_للاسم |
P21030
|
FINISHED |
| Object | العربية |
—
|
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: العربية | Statement: [سيحون, اللغة_التقليدية_للاسم, العربية]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: اللغة_التقليدية_للاسم Context triple: [سيحون, اللغة_التقليدية_للاسم, العربية]
-
A.
traditionalLanguageName
chosen
Indicates the name traditionally used in a particular language to refer to the subject entity.
-
B.
hasTraditionalName
Indicates that an entity is associated with a name traditionally used or recognized for it, often rooted in long-standing cultural or historical practice.
-
C.
languageFamilyTraditional
Indicates that one entity belongs to, or is classified under, the traditional language family of the other entity.
-
D.
ChineseNameTraditional
Indicates that an entity’s name is given in traditional Chinese characters.
-
E.
pairedTraditionalName
Indicates that two entities are associated as a traditional name pair, typically used together or in customary combination within a cultural or naming convention.
- 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_69ca843b123881909b0e60028475d12d |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd7fcc8b1881908aa6ee13ab195330 |
completed | April 1, 2026, 8:27 p.m. |
| PD | Predicate disambiguation | batch_69cca55caaa8819089c5138e014892d3 |
completed | April 1, 2026, 4:55 a.m. |
Created at: March 30, 2026, 7:52 p.m.