Triple
T724147
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grüner Markt |
E14685
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Fürth |
E44190
|
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: Fürth | Statement: [Grüner Markt, locatedIn, Fürth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fürth Context triple: [Grüner Markt, locatedIn, Fürth]
-
A.
Fürth
chosen
Fürth is a historic city in northern Bavaria, Germany, known for its well-preserved old town and proximity to Nuremberg within the Franconian metropolitan region.
-
B.
Starnberg
Starnberg is a lakeside town in Bavaria, Germany, known for its affluent residential character and scenic location on Lake Starnberg southwest of Munich.
-
C.
Breyten
Breyten is the given name of Breyten Breytenbach, the renowned South African poet, painter, and anti-apartheid activist.
-
D.
Sieber
Sieber is a small river in the German state of Lower Saxony that flows through the Harz Mountains and into the Oder.
-
E.
Schöngarth
Schöngarth is a German surname most notably associated with Eberhard Schöngarth, a high-ranking Nazi SS officer and war criminal during World War II.
- 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_69a4934c753c81909b309027e48b9b3a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a5a6ab508190b70a05a9d77829a5 |
completed | March 1, 2026, 8:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7edf8ecec819081ce513fcf8a6d11 |
completed | March 4, 2026, 8:31 a.m. |
Created at: March 1, 2026, 7:37 p.m.