Triple

T30212513
Position Surface form Disambiguated ID Type / Status
Subject Svedala E768107 entity
Predicate locatedIn P40 FINISHED
Object Svedala Municipality
Svedala Municipality is a local government area in Skåne County in southern Sweden, centered around the town of Svedala and known for its mix of rural landscapes and proximity to the city of Malmö.
E1914613 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: Svedala Municipality | Statement: [Svedala, locatedIn, Svedala 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: Svedala Municipality
Triple: [Svedala, locatedIn, Svedala Municipality]
Generated description
Svedala Municipality is a local government area in Skåne County in southern Sweden, centered around the town of Svedala and known for its mix of rural landscapes and proximity to the city of Malmö.

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_69f2247eb0848190b4032f302d39c0d9 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f67ff24e3c8190bef4ca0707963615 completed May 2, 2026, 10:51 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2798998bc08190a04e70cb90de5154 completed June 9, 2026, 4:37 a.m.
NEDg Description generation batch_6a27997f45fc819085f30cac1be7c33f completed June 9, 2026, 4:41 a.m.
NED2 Entity disambiguation (via description) batch_6a279a2c8d0c8190aa6d61585c23d0ab completed June 9, 2026, 4:44 a.m.
Created at: April 29, 2026, 7:33 p.m.