Triple

T22059862
Position Surface form Disambiguated ID Type / Status
Subject Badr bin Abdullah bin Mohammed bin Farhan Al Saud E545122 entity
Predicate givenName P17 FINISHED
Object Badr
Badr is a Saudi royal and government official, notably serving as the country’s first Minister of Culture.
 E1517076 NE FINISHED

How this triple was built (4 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: Badr | Statement: [Badr bin Abdullah bin Mohammed bin Farhan Al Saud, givenName, Badr]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Badr
Context triple: [Badr bin Abdullah bin Mohammed bin Farhan Al Saud, givenName, Badr]
  • A. Badr
    Badr is a major communications satellite brand used by Arabsat to provide television broadcasting and telecommunication services across the Middle East and surrounding regions.
  • B. Badr
    Badr is a town in western Saudi Arabia historically renowned as the site of the pivotal Battle of Badr in early Islamic history.
  • C. Al-Taybeh
    Al-Taybeh is a town in northern Jordan known as one of the population centers within the Irbid region.
  • D. Az-Zumar
    Az-Zumar is the 39th chapter (sura) of the Qur’an, known for its emphasis on sincere worship of God alone and the contrast between the fates of believers and disbelievers.
  • E. al-Askar
    al-Askar was an early Islamic garrison town in Egypt that served as a military and administrative center near Fustat.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Badr
Triple: [Badr bin Abdullah bin Mohammed bin Farhan Al Saud, givenName, Badr]
Generated description
Badr is a Saudi royal and government official, notably serving as the country’s first Minister of Culture.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Badr
Target entity description: Badr is a Saudi royal and government official, notably serving as the country’s first Minister of Culture.
  • A. Badr
    Badr is a town in western Saudi Arabia historically renowned as the site of the pivotal Battle of Badr in early Islamic history.
  • B. Badr
    Badr is a major communications satellite brand used by Arabsat to provide television broadcasting and telecommunication services across the Middle East and surrounding regions.
  • C. Al-Taybeh
    Al-Taybeh is a town in northern Jordan known as one of the population centers within the Irbid region.
  • D. Az-Zumar
    Az-Zumar is the 39th chapter (sura) of the Qur’an, known for its emphasis on sincere worship of God alone and the contrast between the fates of believers and disbelievers.
  • E. al-Askar
    al-Askar was an early Islamic garrison town in Egypt that served as a military and administrative center near Fustat.
  • F. None of above. chosen

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_69e11e3377c48190890c17407b9527d6 completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f1285ba1c88190b4bc0c73f3cf04e1 completed April 28, 2026, 9:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0a80a92ee081909d5119a99d83e064 completed May 18, 2026, 2:59 a.m.
NEDg Description generation batch_6a0a8187516481909d8075dfc025614e completed May 18, 2026, 3:03 a.m.
NED2 Entity disambiguation (via description) batch_6a0a826075988190a7148ded16f779c0 completed May 18, 2026, 3:07 a.m.
Created at: April 16, 2026, 8:27 p.m.