Triple
T15448490
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Örebro Airport |
E370085
|
entity |
| Predicate | serves |
P98
|
FINISHED |
| Object | Örebro |
E370085
|
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: Örebro | Statement: [Örebro Airport, serves, Örebro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Örebro Context triple: [Örebro Airport, serves, Örebro]
-
A.
Örebro
chosen
Örebro is a historic city in central Sweden known for its medieval castle, university, and role as a regional economic and cultural hub.
-
B.
Norrköping
Norrköping is a historic industrial city in eastern Sweden known for its preserved textile mills, waterways, and cultural institutions.
-
C.
Sundsvall
Sundsvall is a coastal city in central Sweden known as an important industrial and commercial center on the Gulf of Bothnia.
-
D.
Nyköping
Nyköping is a historic coastal town in southeastern Sweden known for its medieval castle, harbor, and role as a regional administrative and cultural center.
-
E.
Skövde
Skövde is a town in south-central Sweden that serves as a major military hub and training center for the Swedish Army.
- 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_69d85a19180081909925012fbf4e62a3 |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03ef9334c81908541e231b43eb012 |
completed | April 16, 2026, 1:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00d445c4848190b5c97bb27be6c749 |
completed | May 10, 2026, 6:53 p.m. |
Created at: April 10, 2026, 3:21 a.m.