Triple
T2983615
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hinnøya |
E80568
|
entity |
| Predicate | hasHighestPoint |
P210
|
FINISHED |
| Object |
Møysalen
Møysalen is a prominent mountain in northern Norway known for its rugged alpine scenery and popular hiking routes.
|
E317940
|
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: Møysalen | Statement: [Hinnøya, hasHighestPoint, Møysalen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Møysalen Context triple: [Hinnøya, hasHighestPoint, Møysalen]
-
A.
Mortensrud
Mortensrud is a residential neighborhood in the Søndre Nordstrand borough of Oslo, Norway, known for its multicultural population and modern church, and served as the terminus of an Oslo Metro line.
-
B.
Finnsnes
Finnsnes is a small coastal town in northern Norway that serves as a commercial and transport hub for the island municipality of Senja.
-
C.
Sognsvann
Sognsvann is a popular recreational lake and surrounding forested area in northern Oslo, Norway, known for hiking, swimming, and outdoor activities.
-
D.
Bekkestua
Bekkestua is a suburban center in Bærum, Norway, functioning as a local commercial and transport hub just west of Oslo.
-
E.
Skøyen
Skøyen is a neighborhood in western Oslo, Norway, known as a busy residential and commercial hub with strong public transport connections.
- 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: Møysalen Triple: [Hinnøya, hasHighestPoint, Møysalen]
Generated description
Møysalen is a prominent mountain in northern Norway known for its rugged alpine scenery and popular hiking routes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Møysalen Target entity description: Møysalen is a prominent mountain in northern Norway known for its rugged alpine scenery and popular hiking routes.
-
A.
Mortensrud
Mortensrud is a residential neighborhood in the Søndre Nordstrand borough of Oslo, Norway, known for its multicultural population and modern church, and served as the terminus of an Oslo Metro line.
-
B.
Finnsnes
Finnsnes is a small coastal town in northern Norway that serves as a commercial and transport hub for the island municipality of Senja.
-
C.
Sognsvann
Sognsvann is a popular recreational lake and surrounding forested area in northern Oslo, Norway, known for hiking, swimming, and outdoor activities.
-
D.
Bekkestua
Bekkestua is a suburban center in Bærum, Norway, functioning as a local commercial and transport hub just west of Oslo.
-
E.
Skøyen
Skøyen is a neighborhood in western Oslo, Norway, known as a busy residential and commercial hub with strong public transport connections.
- 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_69ad8b15f6ac8190be5fd16a33edcb4f |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad99c481fc81909971c96352a881b4 |
completed | March 8, 2026, 3:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b12e31a8188190bd5ad6c9757e7141 |
completed | March 11, 2026, 8:56 a.m. |
| NEDg | Description generation | batch_69b131fc55f88190a6220816f39e7d20 |
completed | March 11, 2026, 9:12 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b1c821b31081908cfa7273a7055188 |
completed | March 11, 2026, 7:53 p.m. |
Created at: March 8, 2026, 2:58 p.m.