Triple

T27476827
Position Surface form Disambiguated ID Type / Status
Subject Awa, Tokushima Prefecture, Japan E693485 entity
Predicate hasFormerName P65 FINISHED
Object Yoshino River Town
Yoshino River Town was a former municipality in Tokushima Prefecture, Japan, that later became part of the city of Awa.
 E1815571 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: Yoshino River Town | Statement: [Awa, Tokushima Prefecture, Japan, hasFormerName, Yoshino River Town]
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: Yoshino River Town
Triple: [Awa, Tokushima Prefecture, Japan, hasFormerName, Yoshino River Town]
Generated description
Yoshino River Town was a former municipality in Tokushima Prefecture, Japan, that later became part of the city of Awa.

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_69ef5381f2648190a2392d0fab833095 completed April 27, 2026, 12:16 p.m.
NER Named-entity recognition batch_69f62e451dd08190b9cbe3a9a2a4ffa6 completed May 2, 2026, 5:03 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1632d8f28c819090cadd66ccb3778e completed May 26, 2026, 11:55 p.m.
NEDg Description generation batch_6a1633a0bd1881908757c68e04bdc509 completed May 26, 2026, 11:58 p.m.
NED2 Entity disambiguation (via description) batch_6a1634122f8c8190af25b6651fd12796 completed May 27, 2026, midnight
Created at: April 27, 2026, 12:57 p.m.