Triple

T33922158
Position Surface form Disambiguated ID Type / Status
Subject Fukuda Yasuo E869642 entity
Predicate spouse P13 FINISHED
Object Fukuda Kiyoko
Fukuda Kiyoko is the wife of former Japanese Prime Minister Yasuo Fukuda and a member of a prominent political family in Japan.
E2288703 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: Fukuda Kiyoko | Statement: [Fukuda Yasuo, spouse, Fukuda Kiyoko]
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: Fukuda Kiyoko
Triple: [Fukuda Yasuo, spouse, Fukuda Kiyoko]
Generated description
Fukuda Kiyoko is the wife of former Japanese Prime Minister Yasuo Fukuda and a member of a prominent political family in Japan.

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_69f349992c508190aa4afa24a086cc8c completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f701ee256881909623d25a2590a5c9 completed May 3, 2026, 8:06 a.m.
NED1 Entity disambiguation (via context triple) batch_6a5acf194508819091f4995b1ae7a1e5 completed July 18, 2026, 12:55 a.m.
NEDg Description generation batch_6a5ad1cd80248190af2b00f6bb01b617 completed July 18, 2026, 1:07 a.m.
NED2 Entity disambiguation (via description) batch_6a5ad3a51f348190a07468a34fbb9074 completed July 18, 2026, 1:15 a.m.
Created at: May 1, 2026, 1:49 a.m.