Triple
T17766092
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hallsberg Municipality |
E443508
|
entity |
| Predicate | administrativeCenter |
P1474
|
FINISHED |
| Object | Hallsberg |
—
|
NE NERFINISHED |
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: Hallsberg | Statement: [Hallsberg Municipality, administrativeCenter, Hallsberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hallsberg Context triple: [Hallsberg Municipality, administrativeCenter, Hallsberg]
-
A.
Hallsberg
chosen
Hallsberg is a Swedish railway town in Örebro County known as a major junction in the national rail network.
-
B.
Hesselberg
Hesselberg is a prominent hill in Bavaria, Germany, known as the highest elevation of the Franconian Alb region.
-
C.
Rosersberg
Rosersberg is a locality in Stockholm County, Sweden, known for its historic Rosersberg Palace and its location near Stockholm Arlanda Airport.
-
D.
Olsborg
Olsborg is a small village in Målselv Municipality in Troms og Finnmark county in northern Norway.
-
E.
Hägersten
Hägersten is a residential district in southern Stockholm, Sweden, known for its mix of apartment blocks, green areas, and proximity to the city center.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9edf16c8190a59ebd245d378f4f |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e485fc03e48190a8044e1b40f66f20 |
completed | April 19, 2026, 7:36 a.m. |
Created at: April 10, 2026, 10:11 a.m.