Triple

T27188950
Position Surface form Disambiguated ID Type / Status
Subject Takahashi River E683414 entity
Predicate hasJapaneseName P9882 FINISHED
Object 高梁川
高梁川 is a river in Okayama Prefecture, Japan, known for flowing through the Takahashi basin and playing an important role in the region’s agriculture and settlements.
 E1759991 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: 高梁川 | Statement: [Takahashi River, hasJapaneseName, 高梁川]
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: 高梁川
Triple: [Takahashi River, hasJapaneseName, 高梁川]
Generated description
高梁川 is a river in Okayama Prefecture, Japan, known for flowing through the Takahashi basin and playing an important role in the region’s agriculture and settlements.

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_69eefad140408190b8586fdebcf9af46 completed April 27, 2026, 5:57 a.m.
NER Named-entity recognition batch_69f625a9e3688190996e628a17d211d0 completed May 2, 2026, 4:26 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12539401d88190b275796f1ae7344f completed May 24, 2026, 1:25 a.m.
NEDg Description generation batch_6a125455f8fc81909ae39b6651a0fdb0 completed May 24, 2026, 1:28 a.m.
NED2 Entity disambiguation (via description) batch_6a12552b0cc88190be6bc59664de20c9 completed May 24, 2026, 1:32 a.m.
Created at: April 27, 2026, 9:31 a.m.