Triple
T19783723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schönbuch |
E475203
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Böblingen |
—
|
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: Böblingen | Statement: [Schönbuch, locatedNear, Böblingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Böblingen Context triple: [Schönbuch, locatedNear, Böblingen]
-
A.
Böblingen
chosen
Böblingen is a town in the German state of Baden-Württemberg, near Stuttgart, known for its automotive and technology industries and its role as a regional economic center.
-
B.
Esslingen am Neckar
Esslingen am Neckar is a historic German town near Stuttgart, renowned for its well-preserved medieval old town, half-timbered houses, and hillside vineyards along the Neckar River.
-
C.
Waiblingen
Waiblingen is a town in the German state of Baden-Württemberg, located near Stuttgart and known as an important regional center in the Rems-Murr district.
-
D.
Bietigheim-Bissingen
Bietigheim-Bissingen is a town in the German state of Baden-Württemberg known for its historic old town, wine-growing tradition, and location near Stuttgart.
-
E.
Baiersbronn
Baiersbronn is a municipality in Germany’s Black Forest renowned for its scenic landscapes and high concentration of Michelin-starred restaurants.
- 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_69d8e51b014081908b263e167370529a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65385ee8081908d58cc3ff05b9b23 |
completed | April 20, 2026, 4:25 p.m. |
Created at: April 10, 2026, 1:49 p.m.