Triple
T11134594
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Slagelse |
E263377
|
entity |
| Predicate | hasNearbyCity |
P350
|
FINISHED |
| Object | Korsør |
E657367
|
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: Korsør | Statement: [Slagelse, hasNearbyCity, Korsør]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Korsør Context triple: [Slagelse, hasNearbyCity, Korsør]
-
A.
Korsør
chosen
Korsør is a Danish coastal town on the island of Zealand, known for its strategic position by the Great Belt strait and its historic maritime and military significance.
-
B.
Vordingborg
Vordingborg is a historic coastal town in southern Denmark known for the ruins of Vordingborg Castle and its prominent Goose Tower.
-
C.
Svendborg
Svendborg is a historic coastal town and seaport in southern Denmark known for its maritime heritage and location on the island of Funen.
-
D.
Frederikshavn
Frederikshavn is a port town in northern Jutland, Denmark, known for its ferry connections to Norway and Sweden and its maritime industry.
-
E.
Nyborg
Nyborg is a historic coastal town and former royal seat in central Denmark, located on the island of Funen.
- 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_69d6aa9c0ba08190bbd19c217489b755 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e85daddc8190a1ae2a4a75cc8d50 |
completed | April 9, 2026, 5:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5562a0f54819095360368672d2e96 |
completed | April 19, 2026, 10:24 p.m. |
Created at: April 8, 2026, 9:28 p.m.