Triple
T2207009
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sarpsborg |
E50822
|
entity |
| Predicate | hasNeighbouringMunicipality |
P224
|
FINISHED |
| Object |
Rakkestad
Rakkestad is a rural municipality in Viken county, southeastern Norway, known for its agriculture and forests.
|
E277705
|
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: Rakkestad | Statement: [Sarpsborg, hasNeighbouringMunicipality, Rakkestad]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rakkestad Context triple: [Sarpsborg, hasNeighbouringMunicipality, Rakkestad]
-
A.
Ringerike
Ringerike is a historic district and municipality in southeastern Norway known for its rich Viking-age heritage and distinctive cultural traditions.
-
B.
Larvik
Larvik is a coastal town and municipality in Vestfold, Norway, known for its harbor, beaches, and historic connections to the shipping and timber industries.
-
C.
Gaustad
Gaustad is a district in Oslo, Norway, known for hosting major academic and research institutions, including parts of the University of Oslo campus.
-
D.
Fredrikstad
Fredrikstad is a coastal city in southeastern Norway known for its well-preserved fortified old town and role as a regional educational and commercial center.
-
E.
Østerås
Østerås is a suburban area in Bærum, Norway, best known as the western endpoint of one of the Oslo Metro lines.
- 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: Rakkestad Triple: [Sarpsborg, hasNeighbouringMunicipality, Rakkestad]
Generated description
Rakkestad is a rural municipality in Viken county, southeastern Norway, known for its agriculture and forests.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rakkestad Target entity description: Rakkestad is a rural municipality in Viken county, southeastern Norway, known for its agriculture and forests.
-
A.
Ringerike
Ringerike is a historic district and municipality in southeastern Norway known for its rich Viking-age heritage and distinctive cultural traditions.
-
B.
Larvik
Larvik is a coastal town and municipality in Vestfold, Norway, known for its harbor, beaches, and historic connections to the shipping and timber industries.
-
C.
Gaustad
Gaustad is a district in Oslo, Norway, known for hosting major academic and research institutions, including parts of the University of Oslo campus.
-
D.
Fredrikstad
Fredrikstad is a coastal city in southeastern Norway known for its well-preserved fortified old town and role as a regional educational and commercial center.
-
E.
Østerås
Østerås is a suburban area in Bærum, Norway, best known as the western endpoint of one of the Oslo Metro lines.
- 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_69a88b06709c8190978fb2418470d1b6 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abbfcbb83081908d5b2f1603c7b4d2 |
completed | March 7, 2026, 6:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af5cc3255c8190bc8de265f452a6b0 |
completed | March 9, 2026, 11:50 p.m. |
| NEDg | Description generation | batch_69af5dc7ef4c81908581716c07dcae47 |
completed | March 9, 2026, 11:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69af5e6449e4819084313a5b44d46044 |
completed | March 9, 2026, 11:57 p.m. |
Created at: March 4, 2026, 7:46 p.m.