Triple
T5406176
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Agder |
E120897
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object |
Farsund
Farsund is a coastal town and municipality in southern Norway known for its maritime heritage, beaches, and historic wooden architecture.
|
E563237
|
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: Farsund | Statement: [Agder, containsTown, Farsund]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Farsund Context triple: [Agder, containsTown, Farsund]
-
A.
Sandefjord
Sandefjord is a coastal town and municipality in southern Norway known for its maritime heritage, whaling history, and popular seaside attractions.
-
B.
Haugesund
Haugesund is a coastal city in southwestern Norway known for its maritime heritage, shipbuilding industry, and annual film and jazz festivals.
-
C.
Bardufoss
Bardufoss is a town in northern Norway known for its military base, including the main headquarters of the Norwegian Army in the region, and its nearby airport.
-
D.
Nordfjordeid
Nordfjordeid is a village in western Norway known as a regional center in Nordfjord and the birthplace of mathematician Sophus Lie.
-
E.
Raufoss
Raufoss is an industrial town in Norway known for its manufacturing sector, particularly in defense and automotive components.
- 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: Farsund Triple: [Agder, containsTown, Farsund]
Generated description
Farsund is a coastal town and municipality in southern Norway known for its maritime heritage, beaches, and historic wooden architecture.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Farsund Target entity description: Farsund is a coastal town and municipality in southern Norway known for its maritime heritage, beaches, and historic wooden architecture.
-
A.
Sandefjord
Sandefjord is a coastal town and municipality in southern Norway known for its maritime heritage, whaling history, and popular seaside attractions.
-
B.
Haugesund
Haugesund is a coastal city in southwestern Norway known for its maritime heritage, shipbuilding industry, and annual film and jazz festivals.
-
C.
Bardufoss
Bardufoss is a town in northern Norway known for its military base, including the main headquarters of the Norwegian Army in the region, and its nearby airport.
-
D.
Nordfjordeid
Nordfjordeid is a village in western Norway known as a regional center in Nordfjord and the birthplace of mathematician Sophus Lie.
-
E.
Raufoss
Raufoss is an industrial town in Norway known for its manufacturing sector, particularly in defense and automotive components.
- 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_69bd46391c0c81909fa484446732b6a3 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd87924c588190beb4a1be27f8d11b |
completed | March 20, 2026, 5:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c11331275c8190950247127d8e33c6 |
completed | March 23, 2026, 10:17 a.m. |
| NEDg | Description generation | batch_69c113c9bc048190ab517300d56dd8e0 |
completed | March 23, 2026, 10:19 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c1144e77f881908ab59a67160c1630 |
completed | March 23, 2026, 10:22 a.m. |
Created at: March 20, 2026, 2:05 p.m.