Triple

T23696077
Position Surface form Disambiguated ID Type / Status
Subject Furesø Municipality E585447 entity
Predicate formedByMergerOf P77 FINISHED
Object Farum Municipality
Farum Municipality was a former local government area in Denmark that existed until it was merged into the newly created Furesø Municipality as part of a nationwide municipal reform.
E1739651 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: Farum Municipality | Statement: [Furesø Municipality, formedByMergerOf, Farum Municipality]
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: Farum Municipality
Triple: [Furesø Municipality, formedByMergerOf, Farum Municipality]
Generated description
Farum Municipality was a former local government area in Denmark that existed until it was merged into the newly created Furesø Municipality as part of a nationwide municipal reform.

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_69e24904bd508190abfcb74855de2918 completed April 17, 2026, 2:51 p.m.
NER Named-entity recognition batch_69f1b5c65ab88190be59a8fb731cf030 completed April 29, 2026, 7:39 a.m.
NED1 Entity disambiguation (via context triple) batch_6a12090c186c8190ace26c8afff630fa completed May 23, 2026, 8:07 p.m.
NEDg Description generation batch_6a1209a175e481909f713b13a9e8d3a0 completed May 23, 2026, 8:10 p.m.
NED2 Entity disambiguation (via description) batch_6a120a18cf84819084da110d13063fe9 completed May 23, 2026, 8:12 p.m.
Created at: April 17, 2026, 6:52 p.m.