Triple

T36383529
Position Surface form Disambiguated ID Type / Status
Subject Ibogawa River E896121 entity
Predicate hasJapaneseName P9882 FINISHED
Object 揖保川
揖保川 is a river in Hyōgo Prefecture, Japan, known for flowing through the Harima region and supporting local agriculture and communities.
E2181186 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: 揖保川 | Statement: [Ibogawa River, hasJapaneseName, 揖保川]
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: 揖保川
Triple: [Ibogawa River, hasJapaneseName, 揖保川]
Generated description
揖保川 is a river in Hyōgo Prefecture, Japan, known for flowing through the Harima region and supporting local agriculture 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_69f76e51d358819092bbc5f119f49476 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7bcd3434c8190b0c31f1ebd225291 completed May 3, 2026, 9:23 p.m.
NED1 Entity disambiguation (via context triple) batch_6a39b4326be481909f06114471bd7cd1 completed June 22, 2026, 10:16 p.m.
NEDg Description generation batch_6a39b4b6c6bc8190a229f76395dbdec3 completed June 22, 2026, 10:18 p.m.
NED2 Entity disambiguation (via description) batch_6a39b55a34108190bf8140198ebc468c completed June 22, 2026, 10:21 p.m.
Created at: May 3, 2026, 4:10 p.m.