Triple
T23543344
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Randesund |
E577815
|
entity |
| Predicate | hasBeach |
P1922
|
FINISHED |
| Object |
Hamresanden
Hamresanden is a popular sandy beach and recreational area near Kristiansand on Norway’s southern coast.
|
E1591464
|
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: Hamresanden | Statement: [Randesund, hasBeach, Hamresanden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hamresanden Context triple: [Randesund, hasBeach, Hamresanden]
-
A.
Rødsand
Rødsand is an island located within Lake Femunden in Norway, known for its natural, undeveloped landscape and role in the region’s outdoor recreation.
-
B.
Sandholmen
Sandholmen is an island within Finland’s Pellinge archipelago, known for its coastal nature and maritime surroundings.
-
C.
Rebbeneshamn
Rebbeneshamn is a small coastal village located on the island of Ringvassøya in northern Norway.
-
D.
Strandeiland
Strandeiland is a newly created artificial island in Amsterdam’s IJburg district, developed as part of the city’s waterfront expansion with housing, beaches, and recreational areas.
-
E.
Hedesunda
Hedesunda is a small locality in east-central Sweden known for its rural character and proximity to forests, lakes, and the Dalälven River.
- 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: Hamresanden Triple: [Randesund, hasBeach, Hamresanden]
Generated description
Hamresanden is a popular sandy beach and recreational area near Kristiansand on Norway’s southern coast.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hamresanden Target entity description: Hamresanden is a popular sandy beach and recreational area near Kristiansand on Norway’s southern coast.
-
A.
Rødsand
Rødsand is an island located within Lake Femunden in Norway, known for its natural, undeveloped landscape and role in the region’s outdoor recreation.
-
B.
Sandholmen
Sandholmen is an island within Finland’s Pellinge archipelago, known for its coastal nature and maritime surroundings.
-
C.
Rebbeneshamn
Rebbeneshamn is a small coastal village located on the island of Ringvassøya in northern Norway.
-
D.
Strandeiland
Strandeiland is a newly created artificial island in Amsterdam’s IJburg district, developed as part of the city’s waterfront expansion with housing, beaches, and recreational areas.
-
E.
Hedesunda
Hedesunda is a small locality in east-central Sweden known for its rural character and proximity to forests, lakes, and the Dalälven River.
- 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_69e245f9d5d08190a4a20004e1784e20 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f1ae1dbe188190bc4afe7bfa7cda0f |
completed | April 29, 2026, 7:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0cdf5fc0d08190a2d4dcc0206d4315 |
completed | May 19, 2026, 10:08 p.m. |
| NEDg | Description generation | batch_6a0ce348596481909924931969de8141 |
completed | May 19, 2026, 10:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0ce3e41c60819095760a2c5a224fb3 |
completed | May 19, 2026, 10:27 p.m. |
Created at: April 17, 2026, 6:11 p.m.