Triple
T4535071
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hallingdal |
E107387
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Hemsedal
Hemsedal is a Norwegian mountain village and ski resort area renowned for its alpine terrain and winter sports tourism.
|
E453351
|
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: Hemsedal | Statement: [Hallingdal, contains, Hemsedal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hemsedal Context triple: [Hallingdal, contains, Hemsedal]
-
A.
Engerdal
Engerdal is a sparsely populated municipality in Innlandet county, Norway, known for its vast forests, lakes, and proximity to the Swedish border.
-
B.
Verdal
Verdal is a municipality in central Norway known for its agricultural landscape, industrial activity, and the historic battlefield of Stiklestad.
-
C.
Hallingdal
Hallingdal is a major valley and traditional district in southeastern Norway, known for its river, ski resorts, and rich folk culture.
-
D.
Gjesdal
Gjesdal is a municipality in Rogaland county in southwestern Norway, known for its rural landscapes and proximity to the city of Stavanger.
-
E.
Ullensvang
Ullensvang is a scenic municipality in Vestland county, Norway, known for its fruit orchards, fjord landscapes, and location along the Hardangerfjord.
- 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: Hemsedal Triple: [Hallingdal, contains, Hemsedal]
Generated description
Hemsedal is a Norwegian mountain village and ski resort area renowned for its alpine terrain and winter sports tourism.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hemsedal Target entity description: Hemsedal is a Norwegian mountain village and ski resort area renowned for its alpine terrain and winter sports tourism.
-
A.
Engerdal
Engerdal is a sparsely populated municipality in Innlandet county, Norway, known for its vast forests, lakes, and proximity to the Swedish border.
-
B.
Verdal
Verdal is a municipality in central Norway known for its agricultural landscape, industrial activity, and the historic battlefield of Stiklestad.
-
C.
Hallingdal
Hallingdal is a major valley and traditional district in southeastern Norway, known for its river, ski resorts, and rich folk culture.
-
D.
Gjesdal
Gjesdal is a municipality in Rogaland county in southwestern Norway, known for its rural landscapes and proximity to the city of Stavanger.
-
E.
Ullensvang
Ullensvang is a scenic municipality in Vestland county, Norway, known for its fruit orchards, fjord landscapes, and location along the Hardangerfjord.
- 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_69bd43f922788190b7edfa294e39b178 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd57a2301c8190aa59280a16750156 |
completed | March 20, 2026, 2:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdd38b19a481908b84b49436517387 |
completed | March 20, 2026, 11:08 p.m. |
| NEDg | Description generation | batch_69bdd47b2740819089cd9a3713402499 |
completed | March 20, 2026, 11:12 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bdd4d005a48190bb5049e9cc281011 |
completed | March 20, 2026, 11:14 p.m. |
Created at: March 20, 2026, 1:04 p.m.