Triple
T20726511
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Altmühlsee |
E509447
|
entity |
| Predicate | nearbySettlement |
P350
|
FINISHED |
| Object |
Schlungenhof
Schlungenhof is a small settlement in Bavaria, Germany, situated close to the recreational Altmühlsee lake.
|
E1447044
|
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: Schlungenhof | Statement: [Altmühlsee, nearbySettlement, Schlungenhof]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schlungenhof Context triple: [Altmühlsee, nearbySettlement, Schlungenhof]
-
A.
Schickenhof
Schickenhof is a small locality in Germany best known as the birthplace of Nobel Prize–winning physicist Johannes Stark.
-
B.
Grabenhof
Grabenhof is a notable building complex located on Vienna’s historic Graben street, known for its characteristic 19th-century architecture and prominent city-center location.
-
C.
Schlaifhausen
Schlaifhausen is a small village in Bavaria, Germany, known as the birthplace of Luftwaffe fighter ace Walter Wolfrum.
-
D.
Hartmannshof
Hartmannshof is a locality in Bavaria, Germany, that functions as an outer terminus on the Nuremberg S-Bahn commuter rail network.
-
E.
Egenhausen
Egenhausen is a small municipality in southwestern Germany, known for its rural setting and surrounding natural landscapes.
- 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: Schlungenhof Triple: [Altmühlsee, nearbySettlement, Schlungenhof]
Generated description
Schlungenhof is a small settlement in Bavaria, Germany, situated close to the recreational Altmühlsee lake.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Schlungenhof Target entity description: Schlungenhof is a small settlement in Bavaria, Germany, situated close to the recreational Altmühlsee lake.
-
A.
Schickenhof
Schickenhof is a small locality in Germany best known as the birthplace of Nobel Prize–winning physicist Johannes Stark.
-
B.
Grabenhof
Grabenhof is a notable building complex located on Vienna’s historic Graben street, known for its characteristic 19th-century architecture and prominent city-center location.
-
C.
Schlaifhausen
Schlaifhausen is a small village in Bavaria, Germany, known as the birthplace of Luftwaffe fighter ace Walter Wolfrum.
-
D.
Hartmannshof
Hartmannshof is a locality in Bavaria, Germany, that functions as an outer terminus on the Nuremberg S-Bahn commuter rail network.
-
E.
Egenhausen
Egenhausen is a small municipality in southwestern Germany, known for its rural setting and surrounding natural landscapes.
- 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_69e0b4c4cc648190b45fda6e2b20af56 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c1e8b020819091d5788b90215ead |
completed | April 21, 2026, 12:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08e05b8d6081908bc76627caf05188 |
completed | May 16, 2026, 9:23 p.m. |
| NEDg | Description generation | batch_6a08e13ca768819084c58419e058b111 |
completed | May 16, 2026, 9:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08e1e759ec8190a305662f220a1826 |
completed | May 16, 2026, 9:30 p.m. |
Created at: April 16, 2026, 12:29 p.m.