Triple

T36098973
Position Surface form Disambiguated ID Type / Status
Subject Villenbach E1044145 entity
Predicate governingBody P46 FINISHED
Object municipal council of Villenbach
The municipal council of Villenbach is the elected local governing body responsible for making administrative and policy decisions for the municipality of Villenbach in Germany.
E2168415 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: municipal council of Villenbach | Statement: [Villenbach, governingBody, municipal council of Villenbach]
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: municipal council of Villenbach
Triple: [Villenbach, governingBody, municipal council of Villenbach]
Generated description
The municipal council of Villenbach is the elected local governing body responsible for making administrative and policy decisions for the municipality of Villenbach in Germany.

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_69f76e338e2c8190b7f3bc68bec76349 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7b28eb9f48190af70ef74de96e306 completed May 3, 2026, 8:39 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38d54c827081909ec1924509525351 completed June 22, 2026, 6:25 a.m.
NEDg Description generation batch_6a38d5e83b58819080bd6a95a17f6b2b completed June 22, 2026, 6:27 a.m.
NED2 Entity disambiguation (via description) batch_6a38d681cf388190896a30e2b0939181 completed June 22, 2026, 6:30 a.m.
Created at: May 3, 2026, 4:08 p.m.