Triple
T12108939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clemens Wenceslaus of Saxony |
E288371
|
entity |
| Predicate | deathPlace |
P21
|
FINISHED |
| Object | Marktoberdorf |
E773414
|
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: Marktoberdorf | Statement: [Clemens Wenceslaus of Saxony, deathPlace, Marktoberdorf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marktoberdorf Context triple: [Clemens Wenceslaus of Saxony, deathPlace, Marktoberdorf]
-
A.
Marktoberdorf
chosen
Marktoberdorf is a small Bavarian town in southern Germany known as an administrative and cultural center in the Allgäu region.
-
B.
Altoberndorf
Altoberndorf is a district or locality within the town of Oberndorf am Neckar in the German state of Baden-Württemberg.
-
C.
Betzdorf
Betzdorf is a commune in eastern Luxembourg known for its residential areas, railway facilities, and the presence of Betzdorf Castle.
-
D.
Landersdorf
Landersdorf is a locality within the city of Krems an der Donau in Lower Austria, known as part of its surrounding wine-growing and rural area.
-
E.
Langdorf
Langdorf is a small municipality in the Bavarian Forest region of southeastern Germany.
- 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_69d6ab4a5c448190a110d1273314b21a |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d915632dc48190863e0239cef37e24 |
completed | April 10, 2026, 3:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f60a7330c481909e06468be517cf5f |
completed | May 2, 2026, 2:30 p.m. |
Created at: April 8, 2026, 9:49 p.m.