Triple
T4206567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Telemark |
E93796
|
entity |
| Predicate | includesTown |
P847
|
FINISHED |
| Object |
Seljord
Seljord is a small Norwegian town known for its scenic lake, traditional cultural events, and the local legend of the Seljord Lake serpent.
|
E434038
|
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: Seljord | Statement: [Telemark, includesTown, Seljord]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seljord Context triple: [Telemark, includesTown, Seljord]
-
A.
Verdal
Verdal is a municipality in central Norway known for its agricultural landscape, industrial activity, and the historic battlefield of Stiklestad.
-
B.
Ullensvang
Ullensvang is a scenic municipality in Vestland county, Norway, known for its fruit orchards, fjord landscapes, and location along the Hardangerfjord.
-
C.
Gaustad
Gaustad is a district in Oslo, Norway, known for hosting major academic and research institutions, including parts of the University of Oslo campus.
-
D.
Ringerike
Ringerike is a historic district and municipality in southeastern Norway known for its rich Viking-age heritage and distinctive cultural traditions.
-
E.
Sæbø
Sæbø is a small Norwegian village known for its scenic location amid steep mountains and fjord landscapes in western Norway.
- 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: Seljord Triple: [Telemark, includesTown, Seljord]
Generated description
Seljord is a small Norwegian town known for its scenic lake, traditional cultural events, and the local legend of the Seljord Lake serpent.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Seljord Target entity description: Seljord is a small Norwegian town known for its scenic lake, traditional cultural events, and the local legend of the Seljord Lake serpent.
-
A.
Verdal
Verdal is a municipality in central Norway known for its agricultural landscape, industrial activity, and the historic battlefield of Stiklestad.
-
B.
Ullensvang
Ullensvang is a scenic municipality in Vestland county, Norway, known for its fruit orchards, fjord landscapes, and location along the Hardangerfjord.
-
C.
Gaustad
Gaustad is a district in Oslo, Norway, known for hosting major academic and research institutions, including parts of the University of Oslo campus.
-
D.
Ringerike
Ringerike is a historic district and municipality in southeastern Norway known for its rich Viking-age heritage and distinctive cultural traditions.
-
E.
Sæbø
Sæbø is a small Norwegian village known for its scenic location amid steep mountains and fjord landscapes in western Norway.
- 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_69b3451743608190808f41d17ccf2650 |
completed | March 12, 2026, 10:58 p.m. |
| NER | Named-entity recognition | batch_69b3480cfacc81909a2705eb4e9ce8c1 |
completed | March 12, 2026, 11:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5db7cdcdc81909f7b42bade322c67 |
completed | March 14, 2026, 10:04 p.m. |
| NEDg | Description generation | batch_69b5dc3d81a08190b29b633009fab4ff |
completed | March 14, 2026, 10:07 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5e04f356c81909e3f6f2c865e0f99 |
completed | March 14, 2026, 10:25 p.m. |
Created at: March 12, 2026, 11:03 p.m.