Triple
T8476540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leipzig/Halle Airport |
E200407
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Schkeuditz
Schkeuditz is a town in the German state of Saxony, situated between Leipzig and Halle and known as an important regional transport hub.
|
E760314
|
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: Schkeuditz | Statement: [Leipzig/Halle Airport, locatedNear, Schkeuditz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schkeuditz Context triple: [Leipzig/Halle Airport, locatedNear, Schkeuditz]
-
A.
Taufkirchen
Taufkirchen is a municipality in Bavaria, Germany, known for its strong aerospace and defense industry presence.
-
B.
Kulmbach
Kulmbach is a historic Bavarian town in northern Germany renowned for its beer brewing tradition and its hilltop Plassenburg Castle.
-
C.
Donauwörth
Donauwörth is a historic Bavarian town in southern Germany situated at the confluence of the Danube and Lech rivers.
-
D.
Schneizlreuth
Schneizlreuth is a small Bavarian municipality in southeastern Germany, known for its alpine landscapes and location near the Austrian border.
-
E.
Erding
Erding is a Bavarian town northeast of Munich, best known for its historic center, Erdinger Weißbräu brewery, and large thermal spa complex.
- 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: Schkeuditz Triple: [Leipzig/Halle Airport, locatedNear, Schkeuditz]
Generated description
Schkeuditz is a town in the German state of Saxony, situated between Leipzig and Halle and known as an important regional transport hub.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Schkeuditz Target entity description: Schkeuditz is a town in the German state of Saxony, situated between Leipzig and Halle and known as an important regional transport hub.
-
A.
Taufkirchen
Taufkirchen is a municipality in Bavaria, Germany, known for its strong aerospace and defense industry presence.
-
B.
Kulmbach
Kulmbach is a historic Bavarian town in northern Germany renowned for its beer brewing tradition and its hilltop Plassenburg Castle.
-
C.
Donauwörth
Donauwörth is a historic Bavarian town in southern Germany situated at the confluence of the Danube and Lech rivers.
-
D.
Schneizlreuth
Schneizlreuth is a small Bavarian municipality in southeastern Germany, known for its alpine landscapes and location near the Austrian border.
-
E.
Erding
Erding is a Bavarian town northeast of Munich, best known for its historic center, Erdinger Weißbräu brewery, and large thermal spa complex.
- 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_69ca831b17988190a1f3f3413d57b820 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe51e21548190811e3c7ba7b196e5 |
completed | March 31, 2026, 3:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf8889ddcc81909ca45ce6438e3a2b |
completed | April 3, 2026, 9:29 a.m. |
| NEDg | Description generation | batch_69cf8a3d8e548190911d44ee36875d44 |
completed | April 3, 2026, 9:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cf8ae86e1881908a77f660c061bf69 |
completed | April 3, 2026, 9:39 a.m. |
Created at: March 30, 2026, 6:12 p.m.