Triple
T20972752
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Little Wiese |
E516543
|
entity |
| Predicate | hasNameInEnglish |
P3437
|
FINISHED |
| Object | Little Wiese |
—
|
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: Little Wiese | Statement: [Little Wiese, hasNameInEnglish, Little Wiese]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Little Wiese Context triple: [Little Wiese, hasNameInEnglish, Little Wiese]
-
A.
Little Wiese
chosen
Little Wiese is a smaller section or subdivision of the Wiese river system in Central Europe.
-
B.
Kleine Werse
Kleine Werse is a small tributary stream of the Werse River in North Rhine-Westphalia, Germany.
-
C.
Wirsberg
Wirsberg is a small market town in the Upper Franconia region of Bavaria, Germany, known for its scenic location in the Franconian Forest and its historic architecture.
-
D.
Weisselberg
Weisselberg is a surname most prominently associated with Allen Weisselberg, the longtime chief financial officer of the Trump Organization.
-
E.
Kleines Wiesental
Kleines Wiesental is a rural municipality in the Black Forest region of southwestern Germany, known for its scenic valleys, forests, and small villages.
- 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_69e0b4fee5ac8190875fa9ceba1a5e5e |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6fba161d88190b8905891f449004e |
completed | April 21, 2026, 4:22 a.m. |
Created at: April 16, 2026, 1:45 p.m.