Triple

T34628075
Position Surface form Disambiguated ID Type / Status
Subject Matsue City Hall E889193 entity
Predicate administrativeCenterOf P383 FINISHED
Object Matsue municipal government
Matsue municipal government is the local governing body responsible for administering public services, regulations, and development policies within the city of Matsue, Japan.
E2104589 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: Matsue municipal government | Statement: [Matsue City Hall, administrativeCenterOf, Matsue municipal government]
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: Matsue municipal government
Triple: [Matsue City Hall, administrativeCenterOf, Matsue municipal government]
Generated description
Matsue municipal government is the local governing body responsible for administering public services, regulations, and development policies within the city of Matsue, Japan.

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_69f349d64a388190a013cfa9bd33fad7 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f72266236081908b34bf1adcaef143 completed May 3, 2026, 10:24 a.m.
NED1 Entity disambiguation (via context triple) batch_6a37412141348190b6743af6a78e880d completed June 21, 2026, 1:40 a.m.
NEDg Description generation batch_6a37439ff6b08190b214789a9a4a2a1b completed June 21, 2026, 1:51 a.m.
NED2 Entity disambiguation (via description) batch_6a37441b02348190ab252640032b8570 completed June 21, 2026, 1:53 a.m.
Created at: May 1, 2026, 2:04 a.m.