Triple
T14881751
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dalsland |
E350016
|
entity |
| Predicate | hasMajorLake |
P1025
|
FINISHED |
| Object | Stora Le |
E245884
|
NE FINISHED |
How this triple was built (2 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: Stora Le | Statement: [Dalsland, hasMajorLake, Stora Le]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stora Le Context triple: [Dalsland, hasMajorLake, Stora Le]
-
A.
Stora Le
chosen
Stora Le is a large lake in southwestern Sweden, known for its clear waters, scenic forested shores, and opportunities for fishing and outdoor recreation.
-
B.
Namsskogan
Namsskogan is a sparsely populated inland municipality in Trøndelag county, Norway, known for its vast forests, wildlife, and outdoor recreation opportunities.
-
C.
Velkua
Velkua is a former island municipality in southwestern Finland known for its coastal archipelago landscape in the Baltic Sea.
-
D.
Støren
Støren is a village in Trøndelag county, Norway, serving as a local commercial and transportation hub in the Gauldalen valley.
-
E.
Mór
Mór is a town in central Hungary known for its wine production and location between the Vértes and Bakony hills.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d822ee4f408190b6ac3b2fa434f0df |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded5e7c0e48190af2d68a71130585c |
completed | April 15, 2026, 12:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe6b591f3c81909ea8a9217d96e0d2 |
completed | May 8, 2026, 11:01 p.m. |
Created at: April 10, 2026, 1:56 a.m.