Triple

T23107891
Position Surface form Disambiguated ID Type / Status
Subject Otra E576227 entity
Predicate flowsThrough P225 FINISHED
Object Evje og Hornnes municipality
Evje og Hornnes municipality is a small inland municipality in Agder county, southern Norway, known for its river landscapes, mineral resources, and outdoor recreation opportunities.
E1697814 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: Evje og Hornnes municipality | Statement: [Otra, flowsThrough, Evje og Hornnes 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: Evje og Hornnes municipality
Triple: [Otra, flowsThrough, Evje og Hornnes municipality]
Generated description
Evje og Hornnes municipality is a small inland municipality in Agder county, southern Norway, known for its river landscapes, mineral resources, and outdoor recreation opportunities.

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_69e245f4af548190898d434a64a1e774 completed April 17, 2026, 2:38 p.m.
NER Named-entity recognition batch_69f18e0c7b9c8190b1160485eae87c9b completed April 29, 2026, 4:50 a.m.
NED1 Entity disambiguation (via context triple) batch_6a10d9d054248190ba78bbefd1342268 completed May 22, 2026, 10:33 p.m.
NEDg Description generation batch_6a10db772c408190875e23a357eb75d9 completed May 22, 2026, 10:40 p.m.
NED2 Entity disambiguation (via description) batch_6a10dc2096b881909e87c9cc277bc831 completed May 22, 2026, 10:43 p.m.
Created at: April 17, 2026, 3:58 p.m.