Triple

T26064726
Position Surface form Disambiguated ID Type / Status
Subject Korsør E657367 entity
Predicate formerMunicipality P5557 FINISHED
Object Korsør Municipality
Korsør Municipality was a former Danish local government area on the island of Zealand that included the town and surrounding area of Korsør before being merged in the 2007 municipal reform.
E1957528 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: Korsør Municipality | Statement: [Korsør, formerMunicipality, Korsør 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: Korsør Municipality
Triple: [Korsør, formerMunicipality, Korsør Municipality]
Generated description
Korsør Municipality was a former Danish local government area on the island of Zealand that included the town and surrounding area of Korsør before being merged in the 2007 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_69ee5bbd788481909e22bd7153d0c037 completed April 26, 2026, 6:38 p.m.
NER Named-entity recognition batch_69f60696110c8190b48e3769a5836657 completed May 2, 2026, 2:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2a71dfcdc8819093a8beaf6e890f9b completed June 11, 2026, 8:29 a.m.
NEDg Description generation batch_6a2a7277685881909febd93c79afdf40 completed June 11, 2026, 8:31 a.m.
NED2 Entity disambiguation (via description) batch_6a2a8c4acaf081909cacf566566440ce completed June 11, 2026, 10:22 a.m.
Created at: April 26, 2026, 7:22 p.m.