Triple
T1044385
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arendal |
E22542
|
entity |
| Predicate | hasNeighbouringMunicipality |
P224
|
FINISHED |
| Object |
Vegårshei
Vegårshei is a rural municipality in Agder county in southern Norway, known for its forests, lakes, and traditional inland communities.
|
E154623
|
NE FINISHED |
How this triple was built (4 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: Vegårshei | Statement: [Arendal, hasNeighbouringMunicipality, Vegårshei]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vegårshei Context triple: [Arendal, hasNeighbouringMunicipality, Vegårshei]
-
A.
Verdal
Verdal is a municipality in central Norway known for its agricultural landscape, industrial activity, and the historic battlefield of Stiklestad.
-
B.
Gaustad
Gaustad is a district in Oslo, Norway, known for hosting major academic and research institutions, including parts of the University of Oslo campus.
-
C.
Tøyen
Tøyen is a neighborhood in Oslo, Norway, known for its cultural institutions, parks, and educational facilities.
-
D.
Ringerike
Ringerike is a historic district and municipality in southeastern Norway known for its rich Viking-age heritage and distinctive cultural traditions.
-
E.
Kragerø
Kragerø is a coastal town in Norway renowned for its picturesque archipelago, historic wooden buildings, and role as a popular summer holiday destination.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Vegårshei Triple: [Arendal, hasNeighbouringMunicipality, Vegårshei]
Generated description
Vegårshei is a rural municipality in Agder county in southern Norway, known for its forests, lakes, and traditional inland communities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vegårshei Target entity description: Vegårshei is a rural municipality in Agder county in southern Norway, known for its forests, lakes, and traditional inland communities.
-
A.
Verdal
Verdal is a municipality in central Norway known for its agricultural landscape, industrial activity, and the historic battlefield of Stiklestad.
-
B.
Gaustad
Gaustad is a district in Oslo, Norway, known for hosting major academic and research institutions, including parts of the University of Oslo campus.
-
C.
Tøyen
Tøyen is a neighborhood in Oslo, Norway, known for its cultural institutions, parks, and educational facilities.
-
D.
Ringerike
Ringerike is a historic district and municipality in southeastern Norway known for its rich Viking-age heritage and distinctive cultural traditions.
-
E.
Kragerø
Kragerø is a coastal town in Norway renowned for its picturesque archipelago, historic wooden buildings, and role as a popular summer holiday destination.
- F. None of above. chosen
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_69a493d91478819094cc01fb65564bc1 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b84937688190a5899af2104002df |
completed | March 1, 2026, 10:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acc60b052c8190bacb19f0fdc8059a |
completed | March 8, 2026, 12:42 a.m. |
| NEDg | Description generation | batch_69acc6c204a88190a3171898e6e1bb91 |
completed | March 8, 2026, 12:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69acc7d8df108190bf92ca5e33987d04 |
completed | March 8, 2026, 12:50 a.m. |
Created at: March 1, 2026, 7:42 p.m.