Triple

T22925136
Position Surface form Disambiguated ID Type / Status
Subject Tanaka political family E569281 entity
Predicate notableMember P10 FINISHED
Object Tanaka Naoki
Tanaka Naoki is a Japanese politician and prominent member of the influential Tanaka political family.
E2241973 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: Tanaka Naoki | Statement: [Tanaka political family, notableMember, Tanaka Naoki]
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: Tanaka Naoki
Triple: [Tanaka political family, notableMember, Tanaka Naoki]
Generated description
Tanaka Naoki is a Japanese politician and prominent member of the influential Tanaka political family.

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_69e2458f7d008190901dccbaebeaba24 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f180d84fc08190b747c3f9052c657a completed April 29, 2026, 3:54 a.m.
NED1 Entity disambiguation (via context triple) batch_6a40e059637881908724631a5f3e467c completed June 28, 2026, 8:50 a.m.
NEDg Description generation batch_6a40e12996ec8190955a5b3c357027c6 completed June 28, 2026, 8:54 a.m.
NED2 Entity disambiguation (via description) batch_6a40e4872fa48190b7a5e2b0497e01cf completed June 28, 2026, 9:08 a.m.
Created at: April 17, 2026, 3:43 p.m.