Triple

T29466785
Position Surface form Disambiguated ID Type / Status
Subject Saudi Ministry of Justice E747398 entity
Predicate collaboratesWith P37 FINISHED
Object Saudi Bar Association
The Saudi Bar Association is a professional organization that regulates and represents legal practitioners in Saudi Arabia, working to develop the legal profession and uphold professional standards.
E1868480 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: Saudi Bar Association | Statement: [Saudi Ministry of Justice, collaboratesWith, Saudi Bar Association]
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: Saudi Bar Association
Triple: [Saudi Ministry of Justice, collaboratesWith, Saudi Bar Association]
Generated description
The Saudi Bar Association is a professional organization that regulates and represents legal practitioners in Saudi Arabia, working to develop the legal profession and uphold professional standards.

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_69f0bd4125f88190b56104591351619c completed April 28, 2026, 1:59 p.m.
NER Named-entity recognition batch_69f66ba8898c8190abf0cb6790a11dcd completed May 2, 2026, 9:24 p.m.
NED1 Entity disambiguation (via context triple) batch_6a25f115d4788190b7604baf3d9f84ec completed June 7, 2026, 10:30 p.m.
NEDg Description generation batch_6a25f6362f6081909a04ef3fbd5bb67f completed June 7, 2026, 10:52 p.m.
NED2 Entity disambiguation (via description) batch_6a25fa9d98d08190aef6fb0a1779f501 completed June 7, 2026, 11:11 p.m.
Created at: April 28, 2026, 3:53 p.m.