Triple
T14056857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M4 |
E338240
|
entity |
| Predicate | serves |
P98
|
FINISHED |
| Object |
Sydhavn
Sydhavn is a district in Copenhagen, Denmark, known for its former industrial harbor areas now undergoing redevelopment into residential and commercial neighborhoods.
|
E1113222
|
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: Sydhavn | Statement: [M4, serves, Sydhavn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sydhavn Context triple: [M4, serves, Sydhavn]
-
A.
Amaliehaven
Amaliehaven is a small waterfront park and fountain garden in central Copenhagen, known for its formal design and views of the harbor and Amalienborg Palace.
-
B.
Nordhavn
Nordhavn is a harbor-side district in Copenhagen, Denmark, known for its large-scale urban redevelopment into a modern, sustainable waterfront neighborhood.
-
C.
Thurø
Thurø is a small Danish island in the Baltic Sea known for its coastal scenery, beaches, and traditional maritime village atmosphere.
-
D.
Hellerup
Hellerup is a suburban district just north of central Copenhagen, known for its affluent residential areas, seaside location, and role as a key transport and commercial hub.
-
E.
Hankø
Hankø is a small Norwegian island and resort area known for its sailing, summer tourism, and scenic coastal landscapes.
- 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: Sydhavn Triple: [M4, serves, Sydhavn]
Generated description
Sydhavn is a district in Copenhagen, Denmark, known for its former industrial harbor areas now undergoing redevelopment into residential and commercial neighborhoods.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sydhavn Target entity description: Sydhavn is a district in Copenhagen, Denmark, known for its former industrial harbor areas now undergoing redevelopment into residential and commercial neighborhoods.
-
A.
Amaliehaven
Amaliehaven is a small waterfront park and fountain garden in central Copenhagen, known for its formal design and views of the harbor and Amalienborg Palace.
-
B.
Nordhavn
Nordhavn is a harbor-side district in Copenhagen, Denmark, known for its large-scale urban redevelopment into a modern, sustainable waterfront neighborhood.
-
C.
Thurø
Thurø is a small Danish island in the Baltic Sea known for its coastal scenery, beaches, and traditional maritime village atmosphere.
-
D.
Hellerup
Hellerup is a suburban district just north of central Copenhagen, known for its affluent residential areas, seaside location, and role as a key transport and commercial hub.
-
E.
Hankø
Hankø is a small Norwegian island and resort area known for its sailing, summer tourism, and scenic coastal landscapes.
- 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_69d81c67ba6c819091935650dfb3b895 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de3c8e6d008190af8892f34c5cefbd |
completed | April 14, 2026, 1:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fde15cbbb0819099b84032d65cfdb0 |
completed | May 8, 2026, 1:13 p.m. |
| NEDg | Description generation | batch_69fde427c5948190abb2630406a1d51d |
completed | May 8, 2026, 1:24 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fde50a254881908ae84e32f631654a |
completed | May 8, 2026, 1:28 p.m. |
Created at: April 9, 2026, 10:20 p.m.