Triple
T21252433
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uckermark district |
E523776
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Templin |
—
|
NE NERFINISHED |
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: Templin | Statement: [Uckermark district, contains, Templin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Templin Context triple: [Uckermark district, contains, Templin]
-
A.
Templin
chosen
Templin is a historic spa town in northeastern Germany, known for its medieval city walls and scenic location amid lakes and forests.
-
B.
Wykertown
Wykertown is a small unincorporated community located within Frankford Township in Sussex County, New Jersey.
-
C.
Temple Town
Temple Town is the popular nickname of Kumbakonam, a historic South Indian city renowned for its numerous ancient Hindu temples and rich religious heritage.
-
D.
Sensburg
Sensburg is the former German name of the town now known as Mrągowo in northeastern Poland, historically part of East Prussia.
-
E.
Laingsburg
Laingsburg is a small South African town in the Western Cape, known for its location along major transport routes and for a devastating flood in 1981.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b5146c108190adc9adb73e90abff |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7359f5b408190b951adddba83c97a |
completed | April 21, 2026, 8:30 a.m. |
Created at: April 16, 2026, 3:57 p.m.