Triple
T10950681
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EDDH |
E258717
|
entity |
| Predicate | hasName |
P744
|
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: [EDDH, hasName, Hamburg Airport]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hamburg Airport Context triple: [EDDH, hasName, 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.
Bremen Airport
Bremen Airport is an international airport in northern Germany serving the city of Bremen and the surrounding region with domestic and European flights.
-
E.
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.
- 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_69d6aa88500c819097d7032ca578e74f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d770ed2f1c819081ec58457f57889d |
completed | April 9, 2026, 9:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3447d8cc88190a3e28f204a93a7d3 |
completed | April 18, 2026, 8:44 a.m. |
Created at: April 8, 2026, 9:23 p.m.