Triple
T14601580
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ringsaker |
E342716
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object |
Furnes
Furnes is a village in Ringsaker Municipality in Innlandet county, Norway, known for its rural character and proximity to the town of Hamar.
|
E1108823
|
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: Furnes | Statement: [Ringsaker, hasSettlement, Furnes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Furnes Context triple: [Ringsaker, hasSettlement, Furnes]
-
A.
Fosnes
Fosnes was a former rural municipality in Trøndelag county, Norway, known for its coastal landscape and small, dispersed population.
-
B.
Svolvær
Svolvær is a coastal town in northern Norway that serves as a key fishing, tourism, and transport hub in the Lofoten archipelago.
-
C.
Farsund
Farsund is a coastal town and municipality in southern Norway known for its maritime heritage, beaches, and historic wooden architecture.
-
D.
Berlevåg
Berlevåg is a small coastal town and municipality in Troms og Finnmark county in northern Norway, known for its fishing industry and exposed location on the Barents Sea.
-
E.
Bremsnes
Bremsnes is a village on the island of Averøya in Møre og Romsdal county, Norway, known for its coastal setting and local church.
- 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: Furnes Triple: [Ringsaker, hasSettlement, Furnes]
Generated description
Furnes is a village in Ringsaker Municipality in Innlandet county, Norway, known for its rural character and proximity to the town of Hamar.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Furnes Target entity description: Furnes is a village in Ringsaker Municipality in Innlandet county, Norway, known for its rural character and proximity to the town of Hamar.
-
A.
Fosnes
Fosnes was a former rural municipality in Trøndelag county, Norway, known for its coastal landscape and small, dispersed population.
-
B.
Svolvær
Svolvær is a coastal town in northern Norway that serves as a key fishing, tourism, and transport hub in the Lofoten archipelago.
-
C.
Farsund
Farsund is a coastal town and municipality in southern Norway known for its maritime heritage, beaches, and historic wooden architecture.
-
D.
Berlevåg
Berlevåg is a small coastal town and municipality in Troms og Finnmark county in northern Norway, known for its fishing industry and exposed location on the Barents Sea.
-
E.
Bremsnes
Bremsnes is a village on the island of Averøya in Møre og Romsdal county, Norway, known for its coastal setting and local church.
- 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_69d822dec68081908c2553145c4051dc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb438748081908020ce04b869866a |
completed | April 14, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd94cc9fbc819090ae4efe9bc618aa |
completed | May 8, 2026, 7:46 a.m. |
| NEDg | Description generation | batch_69fd973985b881908f0c2fd201db8104 |
completed | May 8, 2026, 7:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd9843857c819089eb96564b8a9503 |
completed | May 8, 2026, 8:01 a.m. |
Created at: April 10, 2026, 1:25 a.m.