Triple
T16381356
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gustavstraße (Fürth) |
E397814
|
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: [Gustavstraße (Fürth), locatedIn, Fürth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fürth Context triple: [Gustavstraße (Fürth), 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.
Borgentreich
Borgentreich is a small town in North Rhine-Westphalia, Germany, known for its rural character and historic churches.
-
C.
Freyung
Freyung is a small town in southeastern Bavaria, Germany, known as a gateway to the Bavarian Forest region.
-
D.
Bavier
Bavier is the surname of Frances Bavier, the American actress best known for playing Aunt Bee on the classic television series "The Andy Griffith Show."
-
E.
Idstein
Idstein is a historic town in the German state of Hesse, known for its well-preserved medieval old town and timber-framed architecture.
- 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_69d87f2880b48190ae1a9673a3bbef80 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e319dd0e0c8190812bde6a2f7d9644 |
completed | April 18, 2026, 5:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0035689ef08190ba980a359498ca56 |
completed | May 10, 2026, 7:36 a.m. |
Created at: April 10, 2026, 5:08 a.m.