Triple
T2036996
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hans Christian Ørsted |
E44653
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object |
Langeland
Langeland is a Danish island in the South Funen Archipelago, known for its rural landscapes, coastal scenery, and historical villages.
|
E228021
|
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: Langeland | Statement: [Hans Christian Ørsted, placeOfBirth, Langeland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Langeland Context triple: [Hans Christian Ørsted, placeOfBirth, Langeland]
-
A.
Bornholm
Bornholm is a Danish island known for its rocky coastline, medieval ruins, and picturesque fishing villages in the Baltic Sea.
-
B.
Møn
Møn is a Danish island in the Baltic Sea known for its dramatic white chalk cliffs, scenic landscapes, and rich prehistoric and cultural heritage.
-
C.
Læsø
Læsø is a Danish island in the Kattegat known for its salt production, distinctive seaweed-roofed houses, and tranquil coastal landscapes.
-
D.
Lolland
Lolland is a large, predominantly agricultural island in southeastern Denmark known for its flat landscape and sugar beet production.
-
E.
North Jutlandic Island
North Jutlandic Island is a large island in northern Denmark separated from the rest of Jutland by the Limfjord and known for its coastal landscapes and tourism.
- 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: Langeland Triple: [Hans Christian Ørsted, placeOfBirth, Langeland]
Generated description
Langeland is a Danish island in the South Funen Archipelago, known for its rural landscapes, coastal scenery, and historical villages.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Langeland Target entity description: Langeland is a Danish island in the South Funen Archipelago, known for its rural landscapes, coastal scenery, and historical villages.
-
A.
Bornholm
Bornholm is a Danish island known for its rocky coastline, medieval ruins, and picturesque fishing villages in the Baltic Sea.
-
B.
Møn
Møn is a Danish island in the Baltic Sea known for its dramatic white chalk cliffs, scenic landscapes, and rich prehistoric and cultural heritage.
-
C.
Læsø
Læsø is a Danish island in the Kattegat known for its salt production, distinctive seaweed-roofed houses, and tranquil coastal landscapes.
-
D.
Lolland
Lolland is a large, predominantly agricultural island in southeastern Denmark known for its flat landscape and sugar beet production.
-
E.
North Jutlandic Island
North Jutlandic Island is a large island in northern Denmark separated from the rest of Jutland by the Limfjord and known for its coastal landscapes and tourism.
- 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_69a889159ec481908f9e4472d9f480c7 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb9363b608190b42aa6d3f3fd78c4 |
completed | March 7, 2026, 5:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae1ff38fb881909558e4d715a4d314 |
completed | March 9, 2026, 1:18 a.m. |
| NEDg | Description generation | batch_69ae20946a288190a3bd2a19e3608e86 |
completed | March 9, 2026, 1:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae2109d17c819094a298a822064052 |
completed | March 9, 2026, 1:23 a.m. |
Created at: March 4, 2026, 7:39 p.m.