Triple
T3701783
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Troms |
E80793
|
entity |
| Predicate | containsFjord |
P5879
|
FINISHED |
| Object |
Malangen
Malangen is a prominent fjord in northern Norway known for its scenic coastal landscapes and proximity to the city of Tromsø in Troms county.
|
E392711
|
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: Malangen | Statement: [Troms, containsFjord, Malangen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Malangen Context triple: [Troms, containsFjord, Malangen]
-
A.
Bremanger
Bremanger is a coastal municipality in Vestland county, Norway, known for its rugged fjord landscape, fishing communities, and scenic beaches like Grotlesanden.
-
B.
Malmøya
Malmøya is a notable island in the Oslofjord known for its natural landscapes and recreational areas near Oslo, Norway.
-
C.
Verdal
Verdal is a municipality in central Norway known for its agricultural landscape, industrial activity, and the historic battlefield of Stiklestad.
-
D.
Kragerø
Kragerø is a coastal town in Norway renowned for its picturesque archipelago, historic wooden buildings, and role as a popular summer holiday destination.
-
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: Malangen Triple: [Troms, containsFjord, Malangen]
Generated description
Malangen is a prominent fjord in northern Norway known for its scenic coastal landscapes and proximity to the city of Tromsø in Troms county.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Malangen Target entity description: Malangen is a prominent fjord in northern Norway known for its scenic coastal landscapes and proximity to the city of Tromsø in Troms county.
-
A.
Bremanger
Bremanger is a coastal municipality in Vestland county, Norway, known for its rugged fjord landscape, fishing communities, and scenic beaches like Grotlesanden.
-
B.
Malmøya
Malmøya is a notable island in the Oslofjord known for its natural landscapes and recreational areas near Oslo, Norway.
-
C.
Verdal
Verdal is a municipality in central Norway known for its agricultural landscape, industrial activity, and the historic battlefield of Stiklestad.
-
D.
Kragerø
Kragerø is a coastal town in Norway renowned for its picturesque archipelago, historic wooden buildings, and role as a popular summer holiday destination.
-
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_69ad8b1793888190a5f70e4b21dc05a1 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adc547c1848190a1ece46c59b7c43d |
completed | March 8, 2026, 6:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b503e201e88190bbac29e6b3722959 |
completed | March 14, 2026, 6:44 a.m. |
| NEDg | Description generation | batch_69b505420de0819086dee340f34a8886 |
completed | March 14, 2026, 6:50 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5064192a48190a0f95dee872437e0 |
completed | March 14, 2026, 6:54 a.m. |
Created at: March 8, 2026, 3:33 p.m.