Triple
T9833931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marburg-Biedenkopf |
E239053
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object | Biedenkopf |
E836937
|
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: Biedenkopf | Statement: [Marburg-Biedenkopf, containsTown, Biedenkopf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Biedenkopf Context triple: [Marburg-Biedenkopf, containsTown, Biedenkopf]
-
A.
Biedenkopf
chosen
Biedenkopf is a small historic town in the German state of Hesse, known for its medieval old town and hilltop castle.
-
B.
Köstendorf
Köstendorf is a small Austrian municipality in the state of Salzburg, known for its rural character and proximity to the city of Salzburg.
-
C.
Bernlohe
Bernlohe is a village-level district that forms part of the town of Roth in Bavaria, Germany.
-
D.
Olbernhau
Olbernhau is a town in Germany’s Ore Mountains renowned for its traditional woodcraft industry, especially the production of Schwibbogen candle arches and other Christmas decorations.
-
E.
Ochsenfeld
Ochsenfeld is a German surname most notably borne by physicist Robert Ochsenfeld, known for his work on superconductivity.
- 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_69ca84e314108190978324a4bdb959f8 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3385054819094145c96204e3f0d |
completed | April 2, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d299c51ea08190902e03552fbe7ebb |
completed | April 5, 2026, 5:20 p.m. |
Created at: March 30, 2026, 8:32 p.m.