Triple
T4128965
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bad Waldsee |
E84993
|
entity |
| Predicate | hasLake |
P1025
|
FINISHED |
| Object |
Stadtsee
Stadtsee is a small lake located in the town of Bad Waldsee in southern Germany, known for its scenic setting and recreational use.
|
E433617
|
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: Stadtsee | Statement: [Bad Waldsee, hasLake, Stadtsee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stadtsee Context triple: [Bad Waldsee, hasLake, Stadtsee]
-
A.
Lake Heiligensee
Lake Heiligensee is a small freshwater lake in the Heiligensee district of Berlin, Germany, known for its recreational use and scenic natural surroundings.
-
B.
Schlachtensee
Schlachtensee is a lake and popular recreational area in southwestern Berlin, known for swimming, walking trails, and its surrounding forested landscape.
-
C.
Kettwiger See
Kettwiger See is a reservoir on the Ruhr River in North Rhine-Westphalia, Germany, used for water management, recreation, and local energy production.
-
D.
Schwansee
Schwansee is a picturesque alpine lake in Bavaria, Germany, known for its scenic setting near Neuschwanstein and Hohenschwangau castles.
-
E.
Lake Tegel
Lake Tegel is a large lake in the northwest of Berlin, Germany, known for its recreational areas, beaches, and surrounding forests.
- 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: Stadtsee Triple: [Bad Waldsee, hasLake, Stadtsee]
Generated description
Stadtsee is a small lake located in the town of Bad Waldsee in southern Germany, known for its scenic setting and recreational use.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Stadtsee Target entity description: Stadtsee is a small lake located in the town of Bad Waldsee in southern Germany, known for its scenic setting and recreational use.
-
A.
Lake Heiligensee
Lake Heiligensee is a small freshwater lake in the Heiligensee district of Berlin, Germany, known for its recreational use and scenic natural surroundings.
-
B.
Schlachtensee
Schlachtensee is a lake and popular recreational area in southwestern Berlin, known for swimming, walking trails, and its surrounding forested landscape.
-
C.
Kettwiger See
Kettwiger See is a reservoir on the Ruhr River in North Rhine-Westphalia, Germany, used for water management, recreation, and local energy production.
-
D.
Schwansee
Schwansee is a picturesque alpine lake in Bavaria, Germany, known for its scenic setting near Neuschwanstein and Hohenschwangau castles.
-
E.
Lake Tegel
Lake Tegel is a large lake in the northwest of Berlin, Germany, known for its recreational areas, beaches, and surrounding forests.
- 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_69aed935ccd881909dc61f81bcdb7a78 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af021c5ca48190a829bab07dda55d0 |
completed | March 9, 2026, 5:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5db72503c81909e9cf69f23d093fc |
completed | March 14, 2026, 10:04 p.m. |
| NEDg | Description generation | batch_69b5dc0ec2f08190b62711a9abb98099 |
completed | March 14, 2026, 10:07 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5dca33e0c81909e5047d8bc700530 |
completed | March 14, 2026, 10:09 p.m. |
Created at: March 9, 2026, 3:42 p.m.