Triple

T20037821
Position Surface form Disambiguated ID Type / Status
Subject Yoshii River E497322 entity
Predicate flowsThrough P225 FINISHED
Object Kagamino
Kagamino is a town in Okayama Prefecture, Japan, known for its rural landscapes and river valleys in the Chūgoku Mountains region.
E1700932 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: Kagamino | Statement: [Yoshii River, flowsThrough, Kagamino]
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: Kagamino
Triple: [Yoshii River, flowsThrough, Kagamino]
Generated description
Kagamino is a town in Okayama Prefecture, Japan, known for its rural landscapes and river valleys in the Chūgoku Mountains region.

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_69da627278c88190babe4297a9df1236 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e662e935ac8190900cdb4f0cfde505 completed April 20, 2026, 5:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10ec6f631c8190b4d549e69a7e1b47 completed May 22, 2026, 11:53 p.m.
NEDg Description generation batch_6a10edf7ff0c8190935a637ff0df364b completed May 22, 2026, 11:59 p.m.
NED2 Entity disambiguation (via description) batch_6a10ef6795e08190a1ba5f600628316b completed May 23, 2026, 12:05 a.m.
Created at: April 11, 2026, 3:36 p.m.