Triple

T31574999
Position Surface form Disambiguated ID Type / Status
Subject Elsendorf E805673 entity
Predicate governingBody P46 FINISHED
Object municipal council of Elsendorf
The municipal council of Elsendorf is the elected local governing body responsible for making administrative and policy decisions for the municipality of Elsendorf in Germany.
E1967693 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 Elsendorf | Statement: [Elsendorf, governingBody, municipal council of Elsendorf]
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 Elsendorf
Triple: [Elsendorf, governingBody, municipal council of Elsendorf]
Generated description
The municipal council of Elsendorf is the elected local governing body responsible for making administrative and policy decisions for the municipality of Elsendorf 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_69f348d3a86c8190a3e5e539a4dd125f completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6a7eb3f348190b29fc017d9419b56 completed May 3, 2026, 1:42 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2b2d9ef008819080a3436f77b0bea8 completed June 11, 2026, 9:50 p.m.
NEDg Description generation batch_6a2b316024bc8190ba9d3b95abffe713 completed June 11, 2026, 10:06 p.m.
NED2 Entity disambiguation (via description) batch_6a2b3222dbac8190a3a5c1b3b924306a completed June 11, 2026, 10:09 p.m.
Created at: April 30, 2026, 10:21 p.m.