Triple
T9180793
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Freudenstadt |
E220321
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Baiersbronn |
E421254
|
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: Baiersbronn | Statement: [Freudenstadt, locatedNear, Baiersbronn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Baiersbronn Context triple: [Freudenstadt, locatedNear, Baiersbronn]
-
A.
Baiersbronn
chosen
Baiersbronn is a municipality in Germany’s Black Forest renowned for its scenic landscapes and high concentration of Michelin-starred restaurants.
-
B.
Blaubeuren
Blaubeuren is a historic town in the Alb-Donau district of Baden-Württemberg, Germany, known for its medieval old town and the karst spring Blautopf.
-
C.
Böblingen
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.
-
D.
Biberach an der Riß
Biberach an der Riß is a historic town in the German state of Baden-Württemberg, known for its well-preserved medieval old town and traditional Swabian culture.
-
E.
Metzingen
Metzingen is a town in the German state of Baden-Württemberg, known for its Swabian heritage and large outlet shopping district.
- 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_69ca83e589948190ac9907819db11ddf |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccc251da448190a5c7f684dac15a84 |
completed | April 1, 2026, 6:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d228318de881909bbf4e68331bb586 |
completed | April 5, 2026, 9:15 a.m. |
Created at: March 30, 2026, 7:23 p.m.