Triple
T27976022
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | سرخپوشان |
E706491
|
entity |
| Predicate | سبک زبانی |
P70575
|
FINISHED |
| Object | غیررسمی و محاورهای |
—
|
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: غیررسمی و محاورهای | Statement: [سرخپوشان, سبک زبانی, غیررسمی و محاورهای]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: سبک زبانی Context triple: [سرخپوشان, سبک زبانی, غیررسمی و محاورهای]
-
A.
سبک
chosen
Indicates a stylistic relationship, where one entity is characterized by, associated with, or exemplifies a particular style or manner of expression in relation to another.
-
B.
stylisticFocus
Indicates a relationship where something is primarily concerned with, emphasizes, or is characterized by a particular style or set of stylistic features.
-
C.
literaryLanguage
Indicates that an entity is expressed, written, or communicated using a particular literary or standardized written language.
-
D.
linguisticFeature
Indicates a relationship where a linguistic property, pattern, or characteristic is attributed to or associated with a language-related entity (such as a word, phrase, or text).
-
E.
stylisticRange
Indicates the range or spectrum of styles that characterize or can be applied to something.
- 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_69ef96b7f330819090f315318ba6977e |
completed | April 27, 2026, 5:02 p.m. |
| NER | Named-entity recognition | batch_69f63b38a5c081908edb1c9a415c914b |
completed | May 2, 2026, 5:58 p.m. |
| PD | Predicate disambiguation | batch_69f63710d17c819084cfe96e6df334fd |
completed | May 2, 2026, 5:40 p.m. |
Created at: April 27, 2026, 7:41 p.m.