Triple
T13423408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bollnäs House |
E313415
|
entity |
| Predicate | originallyFrom |
P11553
|
FINISHED |
| Object | Bollnäs |
E186086
|
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: Bollnäs | Statement: [Bollnäs House, originallyFrom, Bollnäs]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bollnäs Context triple: [Bollnäs House, originallyFrom, Bollnäs]
-
A.
Bollnäs
chosen
Bollnäs is a small Swedish town known for its scenic lakeside setting, traditional wooden architecture, and strong bandy sports culture.
-
B.
Bollstanäs
Bollstanäs is a residential locality in Sweden situated within the suburban area of Upplands Väsby, north of Stockholm.
-
C.
Korsnäs
Korsnäs is a small coastal municipality in western Finland known for its Swedish-speaking majority and traditional Ostrobothnian rural culture.
-
D.
Såtenäs
Såtenäs is a locality in western Sweden best known as a major Swedish Air Force base and home of the F 7 Wing.
-
E.
Tärnsjö
Tärnsjö is a small locality in central Sweden known for its rural setting and traditional leather tanning industry.
- 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_69d806ad0c44819088833ae1ec9e9690 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaecf13748190ae40c7b95164f914 |
completed | April 12, 2026, 2:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7308673488190a64f4b205899605b |
completed | May 3, 2026, 11:24 a.m. |
Created at: April 9, 2026, 9:39 p.m.