Triple
T3417166
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eurowings |
E72036
|
entity |
| Predicate | hub |
P423
|
FINISHED |
| Object | Hamburg Airport |
E44344
|
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: Hamburg Airport | Statement: [Eurowings, hub, Hamburg Airport]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hamburg Airport Context triple: [Eurowings, hub, Hamburg Airport]
-
A.
Hamburg Airport
chosen
Hamburg Airport is an international airport in northern Germany serving the city of Hamburg and the surrounding region as a major passenger and cargo hub.
-
B.
Hannover Airport
Hannover Airport is an international airport serving the city of Hanover in northern Germany, handling passenger and cargo flights for the region.
-
C.
Frankfurt Airport
Frankfurt Airport is one of Europe’s busiest international aviation hubs, serving as a major global gateway and primary airport for the city of Frankfurt am Main in Germany.
-
D.
Munich Airport
Munich Airport is a major international aviation hub in Bavaria, Germany, serving as one of the country’s busiest airports and a key base for Lufthansa.
-
E.
Nuremberg Airport
Nuremberg Airport is an international airport in northern Bavaria, Germany, serving the city of Nuremberg and the surrounding Franconia region with passenger and cargo flights.
- 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_69ad85ad38e48190b7660c5118a35289 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb92c20fc81909b5debced20ec083 |
completed | March 8, 2026, 6 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b44ee17d9881908af3d79b7fb0de9d |
completed | March 13, 2026, 5:52 p.m. |
Created at: March 8, 2026, 3:15 p.m.