Triple
T19391563
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ostalbkreis |
E485080
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object | Ellwangen (Jagst) |
—
|
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: Ellwangen (Jagst) | Statement: [Ostalbkreis, containsTown, Ellwangen (Jagst)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ellwangen (Jagst) Context triple: [Ostalbkreis, containsTown, Ellwangen (Jagst)]
-
A.
Ellwangen (Jagst)
chosen
Ellwangen (Jagst) 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.
-
B.
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.
-
C.
Feuchtwangen
Feuchtwangen is a historic town in Bavaria, Germany, known for its medieval architecture and location along the Romantic Road.
-
D.
Grävenwiesbach
Grävenwiesbach is a municipality in the Hochtaunus region of Hesse, Germany, known for its rural character and proximity to the Taunus mountains.
-
E.
Wuhletal
Wuhletal is a valley landscape in Berlin shaped by the course of the Wuhle river, featuring green spaces, walking paths, and recreational areas.
- 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_69d8e8d460d88190abf0591c5c9d2b0c |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e61b45caec81909dafdf66b361effd |
completed | April 20, 2026, 12:25 p.m. |
Created at: April 10, 2026, 1:36 p.m.