Triple
T3225212
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mecklenburg Lake District |
E67603
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Useriner See
Useriner See is a lake in northeastern Germany, situated within the Mecklenburg Lake District and known for its natural scenery and recreational opportunities.
|
E337247
|
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: Useriner See | Statement: [Mecklenburg Lake District, hasPart, Useriner See]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Useriner See Context triple: [Mecklenburg Lake District, hasPart, Useriner See]
-
A.
Leese
Leese is an English surname most notably associated with British Army General Sir Oliver Leese, a senior commander during the Second World War.
-
B.
Niers
The Niers is a small river in western Germany and the southeastern Netherlands that flows through North Rhine-Westphalia before joining the Meuse (Maas).
-
C.
Isen
Isen is a small town located on Tokunoshima in Japan’s Amami Islands, known for its subtropical climate and coastal scenery.
-
D.
Neste
Neste is a Finnish oil refining and renewable fuels company known for producing sustainable diesel and aviation fuels.
-
E.
Lanman
Lanman is a surname most notably associated with American philanthropist William K. Lanman Jr., a major benefactor of Yale University.
- 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: Useriner See Triple: [Mecklenburg Lake District, hasPart, Useriner See]
Generated description
Useriner See is a lake in northeastern Germany, situated within the Mecklenburg Lake District and known for its natural scenery and recreational opportunities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Useriner See Target entity description: Useriner See is a lake in northeastern Germany, situated within the Mecklenburg Lake District and known for its natural scenery and recreational opportunities.
-
A.
Leese
Leese is an English surname most notably associated with British Army General Sir Oliver Leese, a senior commander during the Second World War.
-
B.
Niers
The Niers is a small river in western Germany and the southeastern Netherlands that flows through North Rhine-Westphalia before joining the Meuse (Maas).
-
C.
Isen
Isen is a small town located on Tokunoshima in Japan’s Amami Islands, known for its subtropical climate and coastal scenery.
-
D.
Neste
Neste is a Finnish oil refining and renewable fuels company known for producing sustainable diesel and aviation fuels.
-
E.
Lanman
Lanman is a surname most notably associated with American philanthropist William K. Lanman Jr., a major benefactor of Yale University.
- 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_69ad858c61888190a31196310d9b30b5 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adae1c51a48190b4a395650528b5d8 |
completed | March 8, 2026, 5:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2625eaa708190b23ca6e575d664a2 |
completed | March 12, 2026, 6:51 a.m. |
| NEDg | Description generation | batch_69b264e25bd48190978a289565854297 |
completed | March 12, 2026, 7:01 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b265cd3fcc8190bc56bbf2de229386 |
completed | March 12, 2026, 7:05 a.m. |
Created at: March 8, 2026, 3:08 p.m.