Triple

T18550186
Position Surface form Disambiguated ID Type / Status
Subject Hemsedal E453351 entity
Predicate partOf P40 FINISHED
Object Hemsedal Municipality
Hemsedal Municipality is a local government area in Viken county, Norway, known for its mountainous landscape and popular ski resort destination in the village of Hemsedal.
E1892752 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: Hemsedal Municipality | Statement: [Hemsedal, partOf, Hemsedal 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: Hemsedal Municipality
Triple: [Hemsedal, partOf, Hemsedal Municipality]
Generated description
Hemsedal Municipality is a local government area in Viken county, Norway, known for its mountainous landscape and popular ski resort destination in the village of Hemsedal.

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_69d8d388b0c881908e610a1c45b52640 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e534beeb408190a03f6d7c3f2ef389 completed April 19, 2026, 8:02 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2713e7d1088190a1bed559fb1658fb completed June 8, 2026, 7:11 p.m.
NEDg Description generation batch_6a2714fad8188190bf86af12ee777b53 completed June 8, 2026, 7:16 p.m.
NED2 Entity disambiguation (via description) batch_6a27198a097c8190aea66eba80acc1d8 completed June 8, 2026, 7:35 p.m.
Created at: April 10, 2026, 11:38 a.m.