Triple
T11134595
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Slagelse |
E263377
|
entity |
| Predicate | hasNearbyCity |
P350
|
FINISHED |
| Object |
Skælskør
Skælskør is a small coastal town in western Zealand, Denmark, known for its historic harbor, scenic fjord, and traditional Danish architecture.
|
E922598
|
NE FINISHED |
How this triple was built (4 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: Skælskør | Statement: [Slagelse, hasNearbyCity, Skælskør]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Skælskør Context triple: [Slagelse, hasNearbyCity, Skælskør]
-
A.
Korsør
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.
Svendborg
Svendborg is a historic coastal town and seaport in southern Denmark known for its maritime heritage and location on the island of Funen.
-
C.
Nykøbing Mors
Nykøbing Mors is a Danish coastal town on the island of Mors, known as its main urban center and a local hub for fishing, trade, and tourism.
-
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.
Oksbøl
Oksbøl is a town in southwestern Jutland, Denmark, known for its military training areas and historical role as a garrison location.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Skælskør Triple: [Slagelse, hasNearbyCity, Skælskør]
Generated description
Skælskør is a small coastal town in western Zealand, Denmark, known for its historic harbor, scenic fjord, and traditional Danish architecture.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Skælskør Target entity description: Skælskør is a small coastal town in western Zealand, Denmark, known for its historic harbor, scenic fjord, and traditional Danish architecture.
-
A.
Korsør
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.
Svendborg
Svendborg is a historic coastal town and seaport in southern Denmark known for its maritime heritage and location on the island of Funen.
-
C.
Nykøbing Mors
Nykøbing Mors is a Danish coastal town on the island of Mors, known as its main urban center and a local hub for fishing, trade, and tourism.
-
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.
Oksbøl
Oksbøl is a town in southwestern Jutland, Denmark, known for its military training areas and historical role as a garrison location.
- F. None of above. chosen
Provenance (5 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_69e58ac418f08190b2936e8dbf9fb27d |
completed | April 20, 2026, 2:09 a.m. |
| NEDg | Description generation | batch_69e59323c5948190bc2c9512f7b0a54f |
completed | April 20, 2026, 2:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e599c53704819097c0fdbbfbbb1e87 |
completed | April 20, 2026, 3:13 a.m. |
Created at: April 8, 2026, 9:28 p.m.