Triple
T12877859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leipzig metropolitan region |
E308012
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Frohburg |
E185208
|
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: Frohburg | Statement: [Leipzig metropolitan region, containsCity, Frohburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Frohburg Context triple: [Leipzig metropolitan region, containsCity, Frohburg]
-
A.
Frohburg
chosen
Frohburg is a small town in the Free State of Saxony in eastern Germany, known for its historic architecture and rural surroundings.
-
B.
Froland
Froland is a rural municipality in Agder county in southern Norway, known for its forests, rivers, and historical ties to the mathematician Niels Henrik Abel.
-
C.
Hofstadt
Hofstadt is the maiden surname of Betty Draper, a central character on the television series "Mad Men."
-
D.
Hollstadt
Hollstadt is a small municipality in the Bavarian district of Rhön-Grabfeld in northern Germany.
-
E.
Waldstadt
Waldstadt is a district of Karlsruhe in the German state of Baden-Württemberg, characterized by its forested setting and primarily residential layout.
- 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_69d7bdf69bc48190af6c2621f28ca351 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d970fa8474819086a8af3c90f3ca84 |
completed | April 10, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f69bb83bac8190838f7537b806317c |
completed | May 3, 2026, 12:50 a.m. |
Created at: April 9, 2026, 5:38 p.m.