Triple
T318155
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bavaria |
E7752
|
entity |
| Predicate | hasLake |
P1025
|
FINISHED |
| Object |
Ammersee
Ammersee is a large glacial lake in southern Germany known for its scenic shores, recreational activities, and proximity to the Alps.
|
E41332
|
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: Ammersee | Statement: [Bavaria, hasLake, Ammersee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ammersee Context triple: [Bavaria, hasLake, Ammersee]
-
A.
Chiemsee
Chiemsee is one of Germany’s largest lakes, famed for its scenic Alpine setting and historic islands such as Herrenchiemsee with its royal palace.
-
B.
Tegeler See
Tegeler See is a large lake in the Tegel district of Berlin, Germany, popular for recreation, boating, and its surrounding natural areas.
-
C.
Lake of Biel
Lake of Biel is a scenic lake in western Switzerland’s Seeland region, known for its vineyards, islands, and role in the Jura water correction system.
-
D.
Lake Balaton
Lake Balaton is a major Central European freshwater lake in western Hungary, renowned as a popular tourist and recreation destination.
-
E.
Wannsee
Wannsee is a lakeside district in southwestern Berlin, Germany, known for its villa colonies, recreational waterfront, and as the site of the infamous 1942 Wannsee Conference.
- 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: Ammersee Triple: [Bavaria, hasLake, Ammersee]
Generated description
Ammersee is a large glacial lake in southern Germany known for its scenic shores, recreational activities, and proximity to the Alps.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ammersee Target entity description: Ammersee is a large glacial lake in southern Germany known for its scenic shores, recreational activities, and proximity to the Alps.
-
A.
Chiemsee
Chiemsee is one of Germany’s largest lakes, famed for its scenic Alpine setting and historic islands such as Herrenchiemsee with its royal palace.
-
B.
Tegeler See
Tegeler See is a large lake in the Tegel district of Berlin, Germany, popular for recreation, boating, and its surrounding natural areas.
-
C.
Lake of Biel
Lake of Biel is a scenic lake in western Switzerland’s Seeland region, known for its vineyards, islands, and role in the Jura water correction system.
-
D.
Lake Balaton
Lake Balaton is a major Central European freshwater lake in western Hungary, renowned as a popular tourist and recreation destination.
-
E.
Wannsee
Wannsee is a lakeside district in southwestern Berlin, Germany, known for its villa colonies, recreational waterfront, and as the site of the infamous 1942 Wannsee Conference.
- 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_69a2e7e7af7881908890039d6be4e9b8 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ea67b7588190be394a56498758b6 |
completed | Feb. 28, 2026, 1:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3cafef7d48190b00f577488298605 |
completed | March 1, 2026, 5:13 a.m. |
| NEDg | Description generation | batch_69a3cb608d5481908e43e0c0d021f925 |
completed | March 1, 2026, 5:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a3cc0fb7b48190bf002516d07b6bc5 |
completed | March 1, 2026, 5:18 a.m. |
Created at: Feb. 28, 2026, 1:08 p.m.