Triple

T38325329
Position Surface form Disambiguated ID Type / Status
Subject Sandefjord E1036766 entity
Predicate mergedWith P77 FINISHED
Object Stokke municipality
Stokke municipality was a former municipality in Vestfold county, Norway, known for its rural character and proximity to the town of Sandefjord before being merged into it.
E2289803 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: Stokke municipality | Statement: [Sandefjord, mergedWith, Stokke 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: Stokke municipality
Triple: [Sandefjord, mergedWith, Stokke municipality]
Generated description
Stokke municipality was a former municipality in Vestfold county, Norway, known for its rural character and proximity to the town of Sandefjord before being merged into it.

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_69f76e1c16fc8190bde982289dd5106b completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69fcc68e20488190b853c31c19954bab completed May 7, 2026, 5:06 p.m.
NED1 Entity disambiguation (via context triple) batch_6a5b6cc757b88190bfddba80689e16c2 completed July 18, 2026, 12:08 p.m.
NEDg Description generation batch_6a5b6d41ab448190a5bf398337e78578 completed July 18, 2026, 12:10 p.m.
NED2 Entity disambiguation (via description) batch_6a5b6e5555188190b43970bcd31160a8 completed July 18, 2026, 12:15 p.m.
Created at: May 3, 2026, 4:30 p.m.