Triple
T3701502
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harstad |
E78588
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object |
Vågsfjorden
Vågsfjorden is a fjord in northern Norway known for its scenic coastal landscapes and as a maritime hub for surrounding towns and islands.
|
E445676
|
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: Vågsfjorden | Statement: [Harstad, locatedIn, Vågsfjorden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vågsfjorden Context triple: [Harstad, locatedIn, Vågsfjorden]
-
A.
Randsfjorden
Randsfjorden is one of Norway’s largest inland lakes, located in Eastern Norway and known for its elongated shape and surrounding forested landscapes.
-
B.
Beisfjorden
Beisfjorden is a fjord in Nordland county, Norway, known as an inner branch of the larger Ofotfjord near the town of Narvik.
-
C.
Bremnesfjorden
Bremnesfjorden is a fjord in Norway known for its coastal landscape along the island of Averøya.
-
D.
Bjørnafjorden
Bjørnafjorden is a large fjord in western Norway known for its scenic coastal landscape and role as an important marine and transport corridor in Vestland county.
-
E.
Boknafjorden
Boknafjorden is a large fjord in Rogaland county in southwestern Norway, known for its many islands and role as an important maritime route.
- 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: Vågsfjorden Triple: [Harstad, locatedIn, Vågsfjorden]
Generated description
Vågsfjorden is a fjord in northern Norway known for its scenic coastal landscapes and as a maritime hub for surrounding towns and islands.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vågsfjorden Target entity description: Vågsfjorden is a fjord in northern Norway known for its scenic coastal landscapes and as a maritime hub for surrounding towns and islands.
-
A.
Randsfjorden
Randsfjorden is one of Norway’s largest inland lakes, located in Eastern Norway and known for its elongated shape and surrounding forested landscapes.
-
B.
Beisfjorden
Beisfjorden is a fjord in Nordland county, Norway, known as an inner branch of the larger Ofotfjord near the town of Narvik.
-
C.
Bremnesfjorden
Bremnesfjorden is a fjord in Norway known for its coastal landscape along the island of Averøya.
-
D.
Bjørnafjorden
Bjørnafjorden is a large fjord in western Norway known for its scenic coastal landscape and role as an important marine and transport corridor in Vestland county.
-
E.
Boknafjorden
Boknafjorden is a large fjord in Rogaland county in southwestern Norway, known for its many islands and role as an important maritime route.
- 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_69ad85e3b1888190abc983e06968696d |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc547c1848190a1ece46c59b7c43d |
completed | March 8, 2026, 6:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bb809c4c6c8190abeb7a5bad4fab67 |
completed | March 19, 2026, 4:50 a.m. |
| NEDg | Description generation | batch_69bb821299448190905e9de00b04e943 |
completed | March 19, 2026, 4:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69bb827d3e788190b65f54a12b4ed354 |
completed | March 19, 2026, 4:58 a.m. |
Created at: March 8, 2026, 3:26 p.m.