Triple
T765969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schloss Tegel |
E16175
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Tegel |
E2522
|
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: Tegel | Statement: [Schloss Tegel, locatedIn, Tegel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tegel Context triple: [Schloss Tegel, locatedIn, Tegel]
-
A.
Tegel
chosen
Tegel is a locality in the Reinickendorf borough of Berlin, Germany, historically known for its manor associated with the Humboldt family and later for the former Berlin Tegel Airport.
-
B.
Tempelhof Airport
Tempelhof Airport is a historic Berlin airfield best known as a central hub of the Berlin Airlift during the Cold War.
-
C.
Berlin Brandenburg Airport
Berlin Brandenburg Airport is the main international airport serving Germany’s capital region, designed to replace and consolidate Berlin’s former commercial airports.
-
D.
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.
-
E.
Hamburg Airport
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.
- 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_69a493684ee48190bd43b7c78da4aec8 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a69fb6ac8190bda41852ea01842c |
completed | March 1, 2026, 8:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a6787a808c81908e8db55a7f28ac90 |
completed | March 3, 2026, 5:58 a.m. |
Created at: March 1, 2026, 7:37 p.m.