Triple

T28209959
Position Surface form Disambiguated ID Type / Status
Subject ذبیح‌الله مجاهد E711131 entity
Predicate نقش در طالبان P119470 FINISHED
Object مسئول اطلاع‌رسانی عمومی طالبان
مسئول اطلاع‌رسانی عمومی طالبان فردی است که به‌عنوان سخنگو و چهره رسانه‌ای این گروه، مواضع و پیام‌های رسمی طالبان را به افکار عمومی و رسانه‌ها منتقل می‌کند.
E1808328 NE 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: مسئول اطلاع‌رسانی عمومی طالبان | Statement: [ذبیح‌الله مجاهد, نقش در طالبان, مسئول اطلاع‌رسانی عمومی طالبان]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: مسئول اطلاع‌رسانی عمومی طالبان
Triple: [ذبیح‌الله مجاهد, نقش در طالبان, مسئول اطلاع‌رسانی عمومی طالبان]
Generated description
مسئول اطلاع‌رسانی عمومی طالبان فردی است که به‌عنوان سخنگو و چهره رسانه‌ای این گروه، مواضع و پیام‌های رسمی طالبان را به افکار عمومی و رسانه‌ها منتقل می‌کند.

Provenance (5 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_69efb51cb5288190818c1f63a266af11 completed April 27, 2026, 7:12 p.m.
NER Named-entity recognition batch_69f64cb36ed88190973a4790577762eb completed May 2, 2026, 7:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a15e6be40b881909be938d11158b49d completed May 26, 2026, 6:30 p.m.
NEDg Description generation batch_6a15eacddeec8190b1bdf14c9b69eecc completed May 26, 2026, 6:47 p.m.
NED2 Entity disambiguation (via description) batch_6a15f22b0b7c8190b575057bea23e19d completed May 26, 2026, 7:19 p.m.
Created at: April 27, 2026, 10:38 p.m.