Triple

T33767195
Position Surface form Disambiguated ID Type / Status
Subject Ali Bongo Ondimba E865267 entity
Predicate hasChild P369 FINISHED
Object Noureddin Bongo Valentin
Noureddin Bongo Valentin is a Gabonese political figure and businessman known as the influential son of former Gabonese president Ali Bongo Ondimba.
E2081885 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: Noureddin Bongo Valentin | Statement: [Ali Bongo Ondimba, hasChild, Noureddin Bongo Valentin]
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: Noureddin Bongo Valentin
Triple: [Ali Bongo Ondimba, hasChild, Noureddin Bongo Valentin]
Generated description
Noureddin Bongo Valentin is a Gabonese political figure and businessman known as the influential son of former Gabonese president Ali Bongo Ondimba.

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_69f3498d3b748190aa3c4006c1f32f38 completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6fc8f9e2c8190b2ee8c1724ddc8b3 completed May 3, 2026, 7:43 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36b748e36c8190a5564ba99a970cce completed June 20, 2026, 3:52 p.m.
NEDg Description generation batch_6a36b87e7e588190abb7ce4c5ea03b0f completed June 20, 2026, 3:57 p.m.
NED2 Entity disambiguation (via description) batch_6a36b9b97df48190bde30fd9c4e0d823 completed June 20, 2026, 4:03 p.m.
Created at: May 1, 2026, 1:45 a.m.