Triple
T1845872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Danish Army |
E41281
|
entity |
| Predicate | headquartersLocation |
P62
|
FINISHED |
| Object |
Karup
Karup is a town in central Jutland, Denmark, notable for its major military air base and role as a key Danish defense hub.
|
E205937
|
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: Karup | Statement: [Danish Army, headquartersLocation, Karup]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Karup Context triple: [Danish Army, headquartersLocation, Karup]
-
A.
Knudstrup
Knudstrup is a small locality in present-day Sweden historically notable as the birthplace of the astronomer Tycho Brahe.
-
B.
Svaneke
Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
-
C.
Gudhjem
Gudhjem is a picturesque coastal village on the Danish island of Bornholm, known for its steep streets, red-roofed houses, and harbor overlooking the Baltic Sea.
-
D.
Maarkedal
Maarkedal is a rural municipality in the Flemish Ardennes of East Flanders, Belgium, known for its hilly landscape and cycling routes.
-
E.
Karinska
Karinska was a renowned 20th-century costume designer best known for her influential work in ballet and theater, particularly with the New York City Ballet.
- 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: Karup Triple: [Danish Army, headquartersLocation, Karup]
Generated description
Karup is a town in central Jutland, Denmark, notable for its major military air base and role as a key Danish defense hub.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Karup Target entity description: Karup is a town in central Jutland, Denmark, notable for its major military air base and role as a key Danish defense hub.
-
A.
Knudstrup
Knudstrup is a small locality in present-day Sweden historically notable as the birthplace of the astronomer Tycho Brahe.
-
B.
Svaneke
Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
-
C.
Gudhjem
Gudhjem is a picturesque coastal village on the Danish island of Bornholm, known for its steep streets, red-roofed houses, and harbor overlooking the Baltic Sea.
-
D.
Maarkedal
Maarkedal is a rural municipality in the Flemish Ardennes of East Flanders, Belgium, known for its hilly landscape and cycling routes.
-
E.
Karinska
Karinska was a renowned 20th-century costume designer best known for her influential work in ballet and theater, particularly with the New York City Ballet.
- 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_69a88648cd44819093303206d96d76ad |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb051640c819088a8b28a03f57331 |
completed | March 7, 2026, 4:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adc9c070548190af52d3feaa3aead2 |
completed | March 8, 2026, 7:10 p.m. |
| NEDg | Description generation | batch_69adcb1466788190bdcb50107d838f83 |
completed | March 8, 2026, 7:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69adcbf5b0b881909d43c748034481f4 |
completed | March 8, 2026, 7:20 p.m. |
Created at: March 4, 2026, 7:33 p.m.