Triple
T16804554
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NUE |
E408444
|
entity |
| Predicate | represents |
P129
|
FINISHED |
| Object | Nuremberg Airport |
E84051
|
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: Nuremberg Airport | Statement: [NUE, represents, Nuremberg Airport]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nuremberg Airport Context triple: [NUE, represents, Nuremberg Airport]
-
A.
Nuremberg Airport
chosen
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.
-
B.
Augsburg Airport
Augsburg Airport is a regional airport in Bavaria, Germany, serving the city of Augsburg and its surrounding area with general aviation and limited commercial services.
-
C.
Stuttgart Airport
Stuttgart Airport is the international airport serving the city of Stuttgart in southwestern Germany, handling both passenger and cargo traffic for the region.
-
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.
Dresden Airport
Dresden Airport is an international airport serving the city of Dresden in eastern Germany, offering passenger and cargo flights and connecting the region to major European destinations.
- 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_69d88393905081908d00a86b99996ac8 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3b2cb68508190a05749bad68f7b43 |
completed | April 18, 2026, 4:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00b28d3a808190bc94a4f09a10da7e |
completed | May 10, 2026, 4:30 p.m. |
Created at: April 10, 2026, 5:22 a.m.