Triple
T6668080
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kagerplassen |
E151655
|
entity |
| Predicate | nearbySettlement |
P350
|
FINISHED |
| Object | Kaag |
E205057
|
NE FINISHED |
How this triple was built (2 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: Kaag | Statement: [Kagerplassen, nearbySettlement, Kaag]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kaag Context triple: [Kagerplassen, nearbySettlement, Kaag]
-
A.
Kaag
chosen
Kaag is a small Dutch village in South Holland known for its island setting in the Kagerplassen lake area and its traditional water sports and boating culture.
-
B.
Kaiten
Kaiten was a Japanese warship that took part in the late-19th-century Boshin War naval engagements, including the Battle of Hakodate.
-
C.
Kogarah
Kogarah is a suburb in southern Sydney, New South Wales, Australia, known as a residential and commercial hub in the St George area.
-
D.
Kagermeer
Kagermeer is a lake in the Kagerplassen lake district in South Holland, Netherlands, popular for boating and watersports.
-
E.
Konna
Konna is a town in central Mali that gained prominence as a strategic battleground during the 2013 conflict between Malian and Islamist forces.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c6f79cf0e48190a69294f3b13a8372 |
completed | March 27, 2026, 9:33 p.m. |
Created at: March 27, 2026, 2:02 p.m.