Triple
T21369988
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maschteich |
E527028
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Maschpark |
—
|
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: Maschpark | Statement: [Maschteich, locatedIn, Maschpark]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maschpark Context triple: [Maschteich, locatedIn, Maschpark]
-
A.
Maschpark
chosen
Maschpark is a historic public park in central Hanover, Germany, known for its landscaped gardens and scenic lake beside the New Town Hall.
-
B.
Germany Park
Germany Park is a public recreational park located in University Park, Texas, offering green space and outdoor amenities for local residents.
-
C.
Matenpark
Matenpark is a public green park located in the Dutch city of Apeldoorn.
-
D.
Alaunpark
Alaunpark is a popular urban green space in Dresden’s Neustadt district, known for its open lawns, recreational areas, and role as a central gathering spot for locals.
-
E.
Othmarschen Park
Othmarschen Park is a green recreational area in the Hamburg district of Othmarschen, known for its landscaped paths, lawns, and local leisure use.
- 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_69e0b51e80808190ba5cb05667af02a9 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b0adbbc081908ed4d839f7cf8f38 |
completed | April 22, 2026, 11:27 a.m. |
Created at: April 16, 2026, 5:10 p.m.