Triple

T35919669
Position Surface form Disambiguated ID Type / Status
Subject Keishōin E1038845 entity
Predicate givenName P17 FINISHED
Object Ofuku
Ofuku, later known as Keishōin, was the influential concubine of Tokugawa Iemitsu and mother of shōgun Tokugawa Tsunayoshi in early Edo-period Japan.
E2281505 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: Ofuku | Statement: [Keishōin, givenName, Ofuku]
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: Ofuku
Triple: [Keishōin, givenName, Ofuku]
Generated description
Ofuku, later known as Keishōin, was the influential concubine of Tokugawa Iemitsu and mother of shōgun Tokugawa Tsunayoshi in early Edo-period 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_69f76e2320748190b7f5c4750d0cd0d3 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7aaa9ba18819082a9b86a87e82f75 completed May 3, 2026, 8:06 p.m.
NED1 Entity disambiguation (via context triple) batch_6a4205a2bac081908d24aca8635307e8 completed June 29, 2026, 5:41 a.m.
NEDg Description generation batch_6a4207fdfa9c81908b46586b1204bd22 completed June 29, 2026, 5:51 a.m.
NED2 Entity disambiguation (via description) batch_6a42086985288190a3cf7b7f45859926 completed June 29, 2026, 5:53 a.m.
Created at: May 3, 2026, 4:07 p.m.