Triple
T4230198
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flughafen |
E94560
|
entity |
| Predicate | adjacentStation |
P5707
|
FINISHED |
| Object |
Ziegelstein
Ziegelstein is a district in Nuremberg, Germany, known for its residential character and proximity to Nuremberg Airport.
|
E421633
|
NE FINISHED |
How this triple was built (4 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: Ziegelstein | Statement: [Flughafen, adjacentStation, Ziegelstein]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ziegelstein Context triple: [Flughafen, adjacentStation, Ziegelstein]
-
A.
Haldenstein
Haldenstein is a small Swiss village in the canton of Graubünden, known in architecture circles as the longtime base of renowned architect Peter Zumthor.
-
B.
Erasbach
Erasbach is a small locality in Bavaria, Germany, best known as the birthplace of the composer Christoph Willibald Gluck.
-
C.
Ziegenberg
Ziegenberg is a locality in Hesse, Germany, historically notable for its role and nearby military installations during the later years of World War II.
-
D.
Schöngarth
Schöngarth is a German surname most notably associated with Eberhard Schöngarth, a high-ranking Nazi SS officer and war criminal during World War II.
-
E.
Hitzacker
Hitzacker is a small historic town in Lower Saxony, Germany, known for its picturesque setting on the Elbe River and its traditional half-timbered architecture.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Ziegelstein Triple: [Flughafen, adjacentStation, Ziegelstein]
Generated description
Ziegelstein is a district in Nuremberg, Germany, known for its residential character and proximity to Nuremberg Airport.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ziegelstein Target entity description: Ziegelstein is a district in Nuremberg, Germany, known for its residential character and proximity to Nuremberg Airport.
-
A.
Haldenstein
Haldenstein is a small Swiss village in the canton of Graubünden, known in architecture circles as the longtime base of renowned architect Peter Zumthor.
-
B.
Erasbach
Erasbach is a small locality in Bavaria, Germany, best known as the birthplace of the composer Christoph Willibald Gluck.
-
C.
Ziegenberg
Ziegenberg is a locality in Hesse, Germany, historically notable for its role and nearby military installations during the later years of World War II.
-
D.
Schöngarth
Schöngarth is a German surname most notably associated with Eberhard Schöngarth, a high-ranking Nazi SS officer and war criminal during World War II.
-
E.
Hitzacker
Hitzacker is a small historic town in Lower Saxony, Germany, known for its picturesque setting on the Elbe River and its traditional half-timbered architecture.
- F. None of above. chosen
Provenance (5 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_69b3453700a08190ae88792e3dc63207 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b34e61ccc081909b880baf1d6a0f24 |
completed | March 12, 2026, 11:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5964f364881908c53cd46af6b1e98 |
completed | March 14, 2026, 5:09 p.m. |
| NEDg | Description generation | batch_69b59731052881908d9358dc629a4018 |
completed | March 14, 2026, 5:13 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b597c529a08190bbf2af92bfef1aa2 |
completed | March 14, 2026, 5:15 p.m. |
Created at: March 12, 2026, 11:05 p.m.