Triple

T26499261
Position Surface form Disambiguated ID Type / Status
Subject Asai Nagamasa E669371 entity
Predicate father P120 FINISHED
Object Azai Hisamasa
Azai Hisamasa was a Sengoku-period Japanese daimyō of the Azai clan and the predecessor of his more famous son, Azai Nagamasa.
E2082168 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: Azai Hisamasa | Statement: [Asai Nagamasa, father, Azai Hisamasa]
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: Azai Hisamasa
Triple: [Asai Nagamasa, father, Azai Hisamasa]
Generated description
Azai Hisamasa was a Sengoku-period Japanese daimyō of the Azai clan and the predecessor of his more famous son, Azai Nagamasa.

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_69eeb319007081909642b414b114b35a completed April 27, 2026, 12:51 a.m.
NER Named-entity recognition batch_69f61359bf448190bca39cbd22a9f023 completed May 2, 2026, 3:08 p.m.
NED1 Entity disambiguation (via context triple) batch_6a36b73ff0d881909364fc488ef21a37 completed June 20, 2026, 3:52 p.m.
NEDg Description generation batch_6a36b81e1e588190bb400c76f45944d1 completed June 20, 2026, 3:56 p.m.
NED2 Entity disambiguation (via description) batch_6a36b9868250819097430b3864d75880 completed June 20, 2026, 4:02 p.m.
Created at: April 27, 2026, 1:11 a.m.