Triple

T17339154
Position Surface form Disambiguated ID Type / Status
Subject Telemark county (re-established) E421018 entity
Predicate borders P224 FINISHED
Object Vestfold county
Vestfold county was a former county in southeastern Norway along the Oslofjord, known for its coastal towns, maritime heritage, and historical Viking sites.
E94296 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: Vestfold county | Statement: [Telemark county (re-established), borders, Vestfold county]
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: Vestfold county
Triple: [Telemark county (re-established), borders, Vestfold county]
Generated description
Vestfold county was a former county in southeastern Norway along the Oslofjord, known for its coastal towns, maritime heritage, and historical Viking sites.

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_69d889d3adc881909319f1edb8d2a956 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e43a14ec90819098db2ac0d58a53e1 completed April 19, 2026, 2:12 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0f4531481c81908b3c1e81d1322994 completed May 21, 2026, 5:47 p.m.
NEDg Description generation batch_6a0f46b69d288190b3fb6dcea9fb44b5 completed May 21, 2026, 5:53 p.m.
NED2 Entity disambiguation (via description) batch_6a0f47974f7c819088de0827ae15dd61 completed May 21, 2026, 5:57 p.m.
Created at: April 10, 2026, 5:44 a.m.