Triple
T14126066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Närke |
E340035
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object | Askersund |
E1012567
|
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: Askersund | Statement: [Närke, containsTown, Askersund]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Askersund Context triple: [Närke, containsTown, Askersund]
-
A.
Askersund
chosen
Askersund is a small Swedish town in Örebro County known for its picturesque harbor setting on the northern shores of Lake Vättern.
-
B.
Svinesund
Svinesund is a strait forming part of the border between Norway and Sweden, best known for its bridges and role as a major road crossing between the two countries.
-
C.
Bogesund
Bogesund is a locality in Sweden known for its surrounding archipelago landscape, forests, and recreational natural areas.
-
D.
Ginnerup
Ginnerup is a small village in Denmark best known as the birthplace of former Danish Prime Minister and NATO Secretary General Anders Fogh Rasmussen.
-
E.
Løgstør
Løgstør is a small Danish town in northern Jutland known for its historic harbor, maritime heritage, and location along the Limfjord.
- 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_69d81c6a95b481909e39111e0c1f31ee |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de6096976481909dc79066c5165a50 |
completed | April 14, 2026, 3:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcdf0c833081908458e4eaee689df7 |
completed | May 7, 2026, 6:50 p.m. |
Created at: April 9, 2026, 10:22 p.m.