Triple
T6668050
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kagerplassen |
E151655
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Kagermeer
Kagermeer is a lake in the Kagerplassen lake district in South Holland, Netherlands, popular for boating and watersports.
|
E610176
|
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: Kagermeer | Statement: [Kagerplassen, hasPart, Kagermeer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kagermeer Context triple: [Kagerplassen, hasPart, Kagermeer]
-
A.
Karosta
Karosta is a historic former military port district in the Latvian city of Liepāja, known for its Tsarist-era fortifications, Soviet naval heritage, and distinctive coastal landscape.
-
B.
Kogarah
Kogarah is a suburb in southern Sydney, New South Wales, Australia, known as a residential and commercial hub in the St George area.
-
C.
Oukaimeden
Oukaimeden is a popular ski resort and mountain destination in the High Atlas Mountains of Morocco, known for its winter sports and scenic alpine landscapes.
-
D.
Gorely
Gorely is an active stratovolcano complex on Russia’s Kamchatka Peninsula, known for its multiple craters, frequent eruptions, and striking acidic crater lakes.
-
E.
Kholmsk
Kholmsk is a port town on the western coast of Sakhalin Island in Russia, serving as an important maritime transport hub in the Sea of Japan.
- 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: Kagermeer Triple: [Kagerplassen, hasPart, Kagermeer]
Generated description
Kagermeer is a lake in the Kagerplassen lake district in South Holland, Netherlands, popular for boating and watersports.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kagermeer Target entity description: Kagermeer is a lake in the Kagerplassen lake district in South Holland, Netherlands, popular for boating and watersports.
-
A.
Karosta
Karosta is a historic former military port district in the Latvian city of Liepāja, known for its Tsarist-era fortifications, Soviet naval heritage, and distinctive coastal landscape.
-
B.
Kogarah
Kogarah is a suburb in southern Sydney, New South Wales, Australia, known as a residential and commercial hub in the St George area.
-
C.
Oukaimeden
Oukaimeden is a popular ski resort and mountain destination in the High Atlas Mountains of Morocco, known for its winter sports and scenic alpine landscapes.
-
D.
Gorely
Gorely is an active stratovolcano complex on Russia’s Kamchatka Peninsula, known for its multiple craters, frequent eruptions, and striking acidic crater lakes.
-
E.
Kholmsk
Kholmsk is a port town on the western coast of Sakhalin Island in Russia, serving as an important maritime transport hub in the Sea of Japan.
- 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_69c687f71fc081909dbd45d6377f6045 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6b0c4a8e48190aa3b2e41902d2f86 |
completed | March 27, 2026, 4:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6ef109f5c8190aa28b5d7aa192e6e |
completed | March 27, 2026, 8:56 p.m. |
| NEDg | Description generation | batch_69c6f0a498cc8190a0494082b91b012d |
completed | March 27, 2026, 9:03 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6f136ac648190b94a7cda43139fd0 |
completed | March 27, 2026, 9:05 p.m. |
Created at: March 27, 2026, 2:02 p.m.