Triple
T20098948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zollernalbkreis |
E496480
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Albstadt |
—
|
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: Albstadt | Statement: [Zollernalbkreis, contains, Albstadt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Albstadt Context triple: [Zollernalbkreis, contains, Albstadt]
-
A.
Albstadt
chosen
Albstadt is a town in the Swabian Jura region of Baden-Württemberg, Germany, known for its textile industry, scenic hiking and cycling routes, and role as a regional economic center.
-
B.
Lautern
Lautern is a historical German locality known as the former residence of Palatine Count John Casimir of Simmern.
-
C.
Ittlingen
Ittlingen is a small municipality in the German state of Baden-Württemberg, located within the Heilbronn region.
-
D.
Freudenstadt
Freudenstadt is a spa and holiday town in southwestern Germany known for its large market square and location in the northern Black Forest.
-
E.
Tuttlingen
Tuttlingen is a town in the state of Baden-Württemberg in southern Germany, known as a major center of the medical technology and surgical instrument industry.
- 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_69da626eee3881909f3454986d4a6511 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6666e306c81909c0ef617e0f6fccf |
completed | April 20, 2026, 5:46 p.m. |
Created at: April 11, 2026, 11:26 p.m.