Triple

T36284153
Position Surface form Disambiguated ID Type / Status
Subject Prince Saud bin Faisal Al Saud E893025 entity
Predicate sibling P363 FINISHED
Object Mohammed bin Faisal Al Saud
Mohammed bin Faisal Al Saud is a Saudi prince and businessman from the House of Saud, known for his roles in banking and investment within Saudi Arabia.
E2276499 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: Mohammed bin Faisal Al Saud | Statement: [Prince Saud bin Faisal Al Saud, sibling, Mohammed bin Faisal Al Saud]
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: Mohammed bin Faisal Al Saud
Triple: [Prince Saud bin Faisal Al Saud, sibling, Mohammed bin Faisal Al Saud]
Generated description
Mohammed bin Faisal Al Saud is a Saudi prince and businessman from the House of Saud, known for his roles in banking and investment within Saudi Arabia.

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_69f76e4955c08190b8cfddca34fc0242 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7b9e086448190acc07a487742e33c completed May 3, 2026, 9:10 p.m.
NED1 Entity disambiguation (via context triple) batch_6a41ea7000008190a5f26bf2a7344391 completed June 29, 2026, 3:45 a.m.
NEDg Description generation batch_6a41eb4f26e481908d2c85e0d36444e2 completed June 29, 2026, 3:49 a.m.
NED2 Entity disambiguation (via description) batch_6a41ebd65c688190bbc848f604bd9e3c completed June 29, 2026, 3:51 a.m.
Created at: May 3, 2026, 4:09 p.m.