Triple
T18597483
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aarhus South district |
E454529
|
entity |
| Predicate | hasNeighborhood |
P40
|
FINISHED |
| Object | Højbjerg |
E1332979
|
NE FINISHED |
How this triple was built (2 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: Højbjerg | Statement: [Aarhus South district, hasNeighborhood, Højbjerg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Højbjerg Context triple: [Aarhus South district, hasNeighborhood, Højbjerg]
-
A.
Højbjerg
chosen
Højbjerg is a suburban district of Aarhus, Denmark, known for its residential areas, green spaces, and cultural attractions.
-
B.
Bent Bruun Kristensen
Bent Bruun Kristensen is a computer scientist known for his work in programming language design, including co-creating the Beta programming language and contributing to object-oriented programming concepts.
-
C.
Aurskog-Høland
Aurskog-Høland is a rural municipality in Viken county, Norway, known for its forests, agriculture, and scattered villages east of Oslo.
-
D.
Rasmus Højlund
Rasmus Højlund is a Danish professional footballer known as a promising young striker who plays in the Premier League and for the Denmark national team.
-
E.
Henrik Ruben Genz
Henrik Ruben Genz is a Danish film director and screenwriter known for works such as "Terribly Happy" and "Chinaman."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d8d38ae7e081908a98df1251842402 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5474d934481909b4afd5ef9031c73 |
completed | April 19, 2026, 9:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a050d6eb92c8190aa0c1f4eb0deb5cb |
completed | May 13, 2026, 11:46 p.m. |
Created at: April 10, 2026, 11:44 a.m.