Triple
T18169633
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ansbach district |
E434988
|
entity |
| Predicate | hasHistoricTown |
P847
|
FINISHED |
| Object | Feuchtwangen |
—
|
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: Feuchtwangen | Statement: [Ansbach district, hasHistoricTown, Feuchtwangen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Feuchtwangen Context triple: [Ansbach district, hasHistoricTown, Feuchtwangen]
-
A.
Feuchtwangen
chosen
Feuchtwangen is a historic town in Bavaria, Germany, known for its medieval architecture and location along the Romantic Road.
-
B.
Vaihingen
Vaihingen is a district in the southwest of Stuttgart, Germany, known for its mix of residential areas, business parks, and proximity to major transport links.
-
C.
Ellwangen
Ellwangen is a historic town in the German state of Baden-Württemberg, known for its well-preserved old town, baroque basilica, and former prince-provost residence.
-
D.
Gerlachsheim
Gerlachsheim is a district of the town Lauda-Königshofen in the Main-Tauber-Kreis region of Baden-Württemberg, Germany.
-
E.
Pfeffenhausen
Pfeffenhausen is a market town in Lower Bavaria, Germany, known for its rural character and location within the Landshut district.
- 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_69d8b90b7a188190b3fc7b8d4a6cd20a |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4df555af081908a2f12bce6a13f56 |
completed | April 19, 2026, 1:57 p.m. |
Created at: April 10, 2026, 10:30 a.m.