Triple

T23276246
Position Surface form Disambiguated ID Type / Status
Subject Kiyoshikōjin Seichō-ji E588726 entity
Predicate dedicatedTo P500 FINISHED
Object Kōjin
Kōjin is a Japanese kami associated with fire, the hearth, and the protection and purification of households and communities.
E1773503 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: Kōjin | Statement: [Kiyoshikōjin Seichō-ji, dedicatedTo, Kōjin]
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: Kōjin
Triple: [Kiyoshikōjin Seichō-ji, dedicatedTo, Kōjin]
Generated description
Kōjin is a Japanese kami associated with fire, the hearth, and the protection and purification of households and communities.

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_69e25d16e2c08190a291de254703129e completed April 17, 2026, 4:17 p.m.
NER Named-entity recognition batch_69f19577841481909acc17bb565bae5c completed April 29, 2026, 5:21 a.m.
NED1 Entity disambiguation (via context triple) batch_6a12b209a2888190ab97a5f521f17322 completed May 24, 2026, 8:08 a.m.
NEDg Description generation batch_6a12b4a525b88190bb16afa9a4ff84c7 completed May 24, 2026, 8:19 a.m.
NED2 Entity disambiguation (via description) batch_6a12b545d37881909ea7fd3c96e8272b completed May 24, 2026, 8:22 a.m.
Created at: April 17, 2026, 4:48 p.m.