Triple
T17370298
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grenland |
E422291
|
entity |
| Predicate | containsMunicipality |
P852
|
FINISHED |
| Object |
Siljan
Siljan is a small rural municipality in Telemark county, Norway, known for its forests, lakes, and outdoor recreation opportunities.
|
E1267161
|
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: Siljan | Statement: [Grenland, containsMunicipality, Siljan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Siljan Context triple: [Grenland, containsMunicipality, Siljan]
-
A.
Siljan
Siljan is a lake in Telemark, Norway, known for its scenic surroundings and proximity to the town of Skien.
-
B.
Siljan Ring
Siljan Ring is a large ancient meteorite impact crater and lake system in central Sweden, known for its distinctive circular landscape and geological significance.
-
C.
Arvidsjaur
Arvidsjaur is a small town in northern Sweden known for its military presence, winter testing facilities, and proximity to Arctic wilderness.
-
D.
Flemingsberg
Flemingsberg is a district in the southern Stockholm urban area known for its major university campus, hospital, and commuter rail hub.
-
E.
Vadstena
Vadstena is a historic Swedish town known for its medieval castle, monastery, and well-preserved old town on the shores of Lake Vättern.
- 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: Siljan Triple: [Grenland, containsMunicipality, Siljan]
Generated description
Siljan is a small rural municipality in Telemark county, Norway, known for its forests, lakes, and outdoor recreation opportunities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Siljan Target entity description: Siljan is a small rural municipality in Telemark county, Norway, known for its forests, lakes, and outdoor recreation opportunities.
-
A.
Siljan
Siljan is a lake in Telemark, Norway, known for its scenic surroundings and proximity to the town of Skien.
-
B.
Siljan Ring
Siljan Ring is a large ancient meteorite impact crater and lake system in central Sweden, known for its distinctive circular landscape and geological significance.
-
C.
Arvidsjaur
Arvidsjaur is a small town in northern Sweden known for its military presence, winter testing facilities, and proximity to Arctic wilderness.
-
D.
Flemingsberg
Flemingsberg is a district in the southern Stockholm urban area known for its major university campus, hospital, and commuter rail hub.
-
E.
Vadstena
Vadstena is a historic Swedish town known for its medieval castle, monastery, and well-preserved old town on the shores of Lake Vättern.
- 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_69d889d6535c81908be333c01deaec4e |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a68ff448190b505861e56df5b6d |
completed | April 19, 2026, 2:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01a7ed9e988190a60106a1a0f94210 |
completed | May 11, 2026, 9:57 a.m. |
| NEDg | Description generation | batch_6a01a886818881909f237f69c6d77f93 |
completed | May 11, 2026, 9:59 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01a9183410819083c8239e3ce38ea2 |
completed | May 11, 2026, 10:02 a.m. |
Created at: April 10, 2026, 5:44 a.m.