Triple

T27650105
Position Surface form Disambiguated ID Type / Status
Subject Al Jiluwi E696826 entity
Predicate hasMember P10 FINISHED
Object Saud bin Abdullah bin Jiluwi
Saud bin Abdullah bin Jiluwi was a Saudi prince from the influential Al Jiluwi branch of the Al Saud family, historically prominent in the governance of Eastern Arabia.
E1946127 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: Saud bin Abdullah bin Jiluwi | Statement: [Al Jiluwi, hasMember, Saud bin Abdullah bin Jiluwi]
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: Saud bin Abdullah bin Jiluwi
Triple: [Al Jiluwi, hasMember, Saud bin Abdullah bin Jiluwi]
Generated description
Saud bin Abdullah bin Jiluwi was a Saudi prince from the influential Al Jiluwi branch of the Al Saud family, historically prominent in the governance of Eastern 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_69ef590abd3c8190834d0193bde12007 completed April 27, 2026, 12:39 p.m.
NER Named-entity recognition batch_69f631d4059c8190ae22a798d2651dcc completed May 2, 2026, 5:18 p.m.
NED1 Entity disambiguation (via context triple) batch_6a293882cd14819083e8fbcff0e5499b completed June 10, 2026, 10:12 a.m.
NEDg Description generation batch_6a29397f80108190b735df8c27bed113 completed June 10, 2026, 10:16 a.m.
NED2 Entity disambiguation (via description) batch_6a2939f6054c8190916e6b8cbdf98c55 completed June 10, 2026, 10:18 a.m.
Created at: April 27, 2026, 2:31 p.m.