Triple

T24468494
Position Surface form Disambiguated ID Type / Status
Subject Ministry of Defense of Syria E617037 entity
Predicate coordinatesWith P1140 FINISHED
Object Ministry of Interior of Syria
The Ministry of Interior of Syria is the government body responsible for internal security, law enforcement, and public order within the Syrian Arab Republic.
E1633734 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: Ministry of Interior of Syria | Statement: [Ministry of Defense of Syria, coordinatesWith, Ministry of Interior of Syria]
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: Ministry of Interior of Syria
Triple: [Ministry of Defense of Syria, coordinatesWith, Ministry of Interior of Syria]
Generated description
The Ministry of Interior of Syria is the government body responsible for internal security, law enforcement, and public order within the Syrian Arab Republic.

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_69e2d7f197588190889a03e620558059 completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f299413ea88190b15e482035ff5a83 completed April 29, 2026, 11:50 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0fe3907328819096fc285ba8716bb5 completed May 22, 2026, 5:03 a.m.
NEDg Description generation batch_6a0fe4dee88081909c792a3463ff3e45 completed May 22, 2026, 5:08 a.m.
NED2 Entity disambiguation (via description) batch_6a0fe572cdd08190a613209dc88d5ad1 completed May 22, 2026, 5:11 a.m.
Created at: April 18, 2026, 2:20 a.m.