Triple
T3701761
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Troms |
E80793
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object |
Lyngseidet
Lyngseidet is a small coastal village in northern Norway, known for its scenic fjord and mountain surroundings on the Lyngen Peninsula.
|
E381852
|
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: Lyngseidet | Statement: [Troms, containsTown, Lyngseidet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lyngseidet Context triple: [Troms, containsTown, Lyngseidet]
-
A.
Aker Brygge
Aker Brygge is a popular waterfront district in Oslo known for its modern architecture, restaurants, shops, and vibrant harbor promenade.
-
B.
Bærum
Bærum is a wealthy suburban municipality just west of Oslo, Norway, known for its high standard of living and residential communities.
-
C.
Ullensaker
Ullensaker is a municipality in Viken county, Norway, best known for hosting Oslo Airport, Gardermoen, the country’s main international airport.
-
D.
Frognerseteren
Frognerseteren is a hilltop area in Oslo, Norway, known for its panoramic views over the city, traditional wooden restaurant, and access to popular hiking and skiing trails.
-
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: Lyngseidet Triple: [Troms, containsTown, Lyngseidet]
Generated description
Lyngseidet is a small coastal village in northern Norway, known for its scenic fjord and mountain surroundings on the Lyngen Peninsula.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lyngseidet Target entity description: Lyngseidet is a small coastal village in northern Norway, known for its scenic fjord and mountain surroundings on the Lyngen Peninsula.
-
A.
Aker Brygge
Aker Brygge is a popular waterfront district in Oslo known for its modern architecture, restaurants, shops, and vibrant harbor promenade.
-
B.
Bærum
Bærum is a wealthy suburban municipality just west of Oslo, Norway, known for its high standard of living and residential communities.
-
C.
Ullensaker
Ullensaker is a municipality in Viken county, Norway, best known for hosting Oslo Airport, Gardermoen, the country’s main international airport.
-
D.
Frognerseteren
Frognerseteren is a hilltop area in Oslo, Norway, known for its panoramic views over the city, traditional wooden restaurant, and access to popular hiking and skiing trails.
-
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_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_69b4cdf53190819098529d11a5a3c7a8 |
completed | March 14, 2026, 2:54 a.m. |
| NEDg | Description generation | batch_69b4cf799ae88190bbf821f4c4500031 |
completed | March 14, 2026, 3:01 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4d0057fe8819092a40732324f88c9 |
completed | March 14, 2026, 3:03 a.m. |
Created at: March 8, 2026, 3:33 p.m.