Triple
T9812672
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hälsingland |
E238313
|
entity |
| Predicate | hasCity |
P316
|
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: [Hälsingland, hasCity, Bollnäs]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bollnäs Context triple: [Hälsingland, hasCity, 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.
Tärnsjö
Tärnsjö is a small locality in central Sweden known for its rural setting and traditional leather tanning industry.
-
E.
Hovsjö
Hovsjö is a residential district in the city of Södertälje, Sweden, known for its large-scale housing estates and diverse population.
- 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_69ca84defac48190abc1148804f184c1 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb222ba788190a9085272a3de7852 |
completed | April 2, 2026, 12:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1d5b264c88190bf16c8c32c360878 |
completed | April 5, 2026, 3:23 a.m. |
Created at: March 30, 2026, 8:30 p.m.