Triple
T20064594
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hohenwarte Dam |
E499574
|
entity |
| Predicate | nearCity |
P350
|
FINISHED |
| Object | Saalfeld |
—
|
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: Saalfeld | Statement: [Hohenwarte Dam, nearCity, Saalfeld]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saalfeld Context triple: [Hohenwarte Dam, nearCity, Saalfeld]
-
A.
Saalfeld
chosen
Saalfeld is a town in the German state of Thuringia, known for its historic old town and former significance as a regional railway and industrial center.
-
B.
Suhl
Suhl is a city in central Germany known historically as a center of firearms manufacturing and located in the federal state of Thuringia.
-
C.
Schmalkalden
Schmalkalden is a historic town in the German state of Thuringia, known for its well-preserved medieval architecture and role in Reformation-era politics.
-
D.
Kronach
Kronach is a historic town in northern Bavaria, Germany, known for its well-preserved medieval old town and the imposing Rosenberg Fortress.
-
E.
Ludwigsstadt
Ludwigsstadt is a small town in northern Bavaria, Germany, known for its location in the Franconian Forest near the Thuringian border.
- 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_69da6276bcf48190aabbf279192a5fb4 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e663787190819096b2b78b38c1bf12 |
completed | April 20, 2026, 5:33 p.m. |
Created at: April 11, 2026, 3:39 p.m.