Triple
T3054800
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aarhus University |
E60454
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Aarhus |
E101867
|
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: Aarhus | Statement: [Aarhus University, locatedIn, Aarhus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aarhus Context triple: [Aarhus University, locatedIn, Aarhus]
-
A.
Aarhus
chosen
Aarhus is Denmark’s second-largest city, a major cultural and economic center on the Jutland peninsula known for its universities, vibrant arts scene, and historic harbor.
-
B.
Aalborg
Aalborg is a major city in northern Denmark known for its historic architecture, vibrant cultural life, and role as a regional economic and educational center.
-
C.
Odense
Odense is a historic Danish city on the island of Funen, best known as the birthplace of fairy-tale author Hans Christian Andersen and a cultural hub with museums, festivals, and a vibrant literary heritage.
-
D.
Randers
Randers is a historic market town and one of the largest cities in eastern Jutland, Denmark, known for its old town center and location along the Gudenå River.
-
E.
Herning
Herning is a Danish city in the Central Jutland region known for its trade fairs, conference facilities, and vibrant cultural and sports events.
- 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_69ad8578137c81908259dcb27c7d6d7c |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ad9bf6b9948190bc957bfd1579c471 |
completed | March 8, 2026, 3:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1f8724bd481909ea8c71e92801096 |
completed | March 11, 2026, 11:19 p.m. |
Created at: March 8, 2026, 3:02 p.m.