Triple
T7561819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Słupsk |
E178809
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object |
Vordingborg
Vordingborg is a historic coastal town in southern Denmark known for the ruins of Vordingborg Castle and its prominent Goose Tower.
|
E690284
|
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: Vordingborg | Statement: [Słupsk, twinTown, Vordingborg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vordingborg Context triple: [Słupsk, twinTown, Vordingborg]
-
A.
Svendborg
Svendborg is a historic coastal town and seaport in southern Denmark known for its maritime heritage and location on the island of Funen.
-
B.
Nyborg
Nyborg is a historic coastal town and former royal seat in central Denmark, located on the island of Funen.
-
C.
Sønderborg
Sønderborg is a coastal town in southern Denmark known for its historic castle, waterfront setting on the island of Als, and role as a regional cultural and educational center.
-
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.
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.
- 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: Vordingborg Triple: [Słupsk, twinTown, Vordingborg]
Generated description
Vordingborg is a historic coastal town in southern Denmark known for the ruins of Vordingborg Castle and its prominent Goose Tower.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vordingborg Target entity description: Vordingborg is a historic coastal town in southern Denmark known for the ruins of Vordingborg Castle and its prominent Goose Tower.
-
A.
Svendborg
Svendborg is a historic coastal town and seaport in southern Denmark known for its maritime heritage and location on the island of Funen.
-
B.
Nyborg
Nyborg is a historic coastal town and former royal seat in central Denmark, located on the island of Funen.
-
C.
Sønderborg
Sønderborg is a coastal town in southern Denmark known for its historic castle, waterfront setting on the island of Als, and role as a regional cultural and educational center.
-
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.
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.
- 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_69c69f2f80288190b95cceb4da92ab2b |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f8f847c48190a1081aa9de7ff945 |
completed | March 27, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c925264798819096e154ffa23ddfae |
completed | March 29, 2026, 1:12 p.m. |
| NEDg | Description generation | batch_69c92685581c8190acc5c259cf89703d |
completed | March 29, 2026, 1:17 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c926d20a24819083d5ff238d9fa0e3 |
completed | March 29, 2026, 1:19 p.m. |
Created at: March 27, 2026, 3:50 p.m.