Triple
T14881734
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Värmland County |
E350015
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Kristinehamn |
E455458
|
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: Kristinehamn | Statement: [Värmland County, hasCity, Kristinehamn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kristinehamn Context triple: [Värmland County, hasCity, Kristinehamn]
-
A.
Kristinehamn
chosen
Kristinehamn is a small Swedish town in Värmland County known for its lakeside location on Vänern and its historical role as a regional trading and industrial center.
-
B.
Kristinestad
Kristinestad is a small coastal town in western Finland known for its well-preserved wooden old town and historic maritime character.
-
C.
Fredrikshamn
Fredrikshamn (Hamina) is a coastal town in southeastern Finland that historically served as an important military and trading center.
-
D.
Söderhamn
Söderhamn is a coastal town in east-central Sweden known for its historical wooden architecture and role as the administrative and commercial center of the surrounding region.
-
E.
Skärhamn
Skärhamn is a coastal town in western Sweden known for its fishing heritage, picturesque harbor, and the Nordic Watercolour Museum.
- 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.