Triple
T19785086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bornsjön |
E475240
|
entity |
| Predicate | hasNearbySettlement |
P4647
|
FINISHED |
| Object | Rönninge |
—
|
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: Rönninge | Statement: [Bornsjön, hasNearbySettlement, Rönninge]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rönninge Context triple: [Bornsjön, hasNearbySettlement, Rönninge]
-
A.
Rönninge
chosen
Rönninge is a locality in Stockholm County, Sweden, serving as the central town of Salem Municipality.
-
B.
Røn
Røn is a small village in Vestre Slidre Municipality in Innlandet county, Norway, known for its scenic lakeside setting and traditional rural character.
-
C.
Löningen
Löningen is a small town and municipality in Lower Saxony, Germany, known for its rural character and location within the Cloppenburg district.
-
D.
Rælingen
Rælingen is a municipality in Viken county, Norway, known for its proximity to Oslo and its mix of residential areas, forests, and lakes.
-
E.
Nannfeldt
Nannfeldt was a mycologist and taxonomist known for his influential work on the classification and nomenclature of fungi, particularly within the Ascomycota.
- 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_69d8e51b014081908b263e167370529a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6538715b8819080c6930e7d16ab58 |
completed | April 20, 2026, 4:25 p.m. |
Created at: April 10, 2026, 1:49 p.m.