Triple

T35530649
Position Surface form Disambiguated ID Type / Status
Subject Nagano City Government E1026790 entity
Predicate hasAdministrativeHead P2537 FINISHED
Object Mayor of Nagano
The Mayor of Nagano is the elected chief executive responsible for leading the municipal administration and representing Nagano City in local governance and public affairs.
E2144380 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: Mayor of Nagano | Statement: [Nagano City Government, hasAdministrativeHead, Mayor of Nagano]
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: Mayor of Nagano
Triple: [Nagano City Government, hasAdministrativeHead, Mayor of Nagano]
Generated description
The Mayor of Nagano is the elected chief executive responsible for leading the municipal administration and representing Nagano City in local governance and public affairs.

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_69f76dff7e508190b28ceeee770dce23 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f797d134f0819086642b4cb807ca88 completed May 3, 2026, 6:45 p.m.
NED1 Entity disambiguation (via context triple) batch_6a384a48bf288190bcaa6ae5e9dff009 completed June 21, 2026, 8:32 p.m.
NEDg Description generation batch_6a384b14247c81909c691fbbfad22c79 completed June 21, 2026, 8:35 p.m.
NED2 Entity disambiguation (via description) batch_6a384b9c09d88190afb8dc6aeb098dbe completed June 21, 2026, 8:37 p.m.
Created at: May 3, 2026, 4:04 p.m.