Triple
T19592528
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Café am Neuen See |
E470271
|
entity |
| Predicate | locatedNextTo |
P231
|
FINISHED |
| Object |
Neuer See
Neuer See is a scenic lake in Berlin’s Tiergarten park, popular for boating and relaxation amid wooded surroundings.
|
E1386368
|
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: Neuer See | Statement: [Café am Neuen See, locatedNextTo, Neuer See]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Neuer See Context triple: [Café am Neuen See, locatedNextTo, Neuer See]
-
A.
Kemnader See
Kemnader See is an artificial lake and popular recreational area on the Ruhr River in western Germany, known for water sports, cycling paths, and leisure activities.
-
B.
Fichtelsee
Fichtelsee is a scenic artificial mountain lake in Germany’s Fichtelgebirge region, popular for hiking, swimming, and nature recreation.
-
C.
Langer See
Langer See is a long, narrow lake in southeastern Berlin that forms part of the city’s interconnected Spree–Dahme waterway and is popular for boating and watersports.
-
D.
Beidersee
Beidersee is a village in the Saalekreis district of Saxony-Anhalt, Germany, that forms part of the town of Wettin-Löbejün.
-
E.
Waldsee
Waldsee is a historic town in southern Germany that served as the ancestral seat of the noble House of Waldburg.
- 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: Neuer See Triple: [Café am Neuen See, locatedNextTo, Neuer See]
Generated description
Neuer See is a scenic lake in Berlin’s Tiergarten park, popular for boating and relaxation amid wooded surroundings.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Neuer See Target entity description: Neuer See is a scenic lake in Berlin’s Tiergarten park, popular for boating and relaxation amid wooded surroundings.
-
A.
Kemnader See
Kemnader See is an artificial lake and popular recreational area on the Ruhr River in western Germany, known for water sports, cycling paths, and leisure activities.
-
B.
Fichtelsee
Fichtelsee is a scenic artificial mountain lake in Germany’s Fichtelgebirge region, popular for hiking, swimming, and nature recreation.
-
C.
Langer See
Langer See is a long, narrow lake in southeastern Berlin that forms part of the city’s interconnected Spree–Dahme waterway and is popular for boating and watersports.
-
D.
Beidersee
Beidersee is a village in the Saalekreis district of Saxony-Anhalt, Germany, that forms part of the town of Wettin-Löbejün.
-
E.
Waldsee
Waldsee is a historic town in southern Germany that served as the ancestral seat of the noble House of Waldburg.
- 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_69d8e510024481908415c0d616fa6186 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e64057460c8190962e2e58f06b3985 |
completed | April 20, 2026, 3:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0771a99edc8190b2ba4fad279109eb |
completed | May 15, 2026, 7:19 p.m. |
| NEDg | Description generation | batch_6a0772ef5f64819096d850a4a2347cd4 |
completed | May 15, 2026, 7:24 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0773543aa881909cf03e024387a595 |
completed | May 15, 2026, 7:26 p.m. |
Created at: April 10, 2026, 1:43 p.m.