Triple
T2777528
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Göttingen district |
E61609
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Hann. Münden |
E177241
|
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: Hann. Münden | Statement: [Göttingen district, contains, Hann. Münden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hann. Münden Context triple: [Göttingen district, contains, Hann. Münden]
-
A.
Hann. Münden
chosen
Hann. Münden is a historic town in Lower Saxony, Germany, renowned for its well-preserved half-timbered architecture and its location where the Fulda and Werra rivers meet to form the Weser.
-
B.
Herrlingen
Herrlingen is a small village in the German state of Baden-Württemberg, historically noted as the place where Field Marshal Erwin Rommel spent his final days during World War II.
-
C.
Hagen
Hagen is a city in the Ruhr region of North Rhine-Westphalia in western Germany, known historically as an industrial and transport hub.
-
D.
Bad Karlshafen
Bad Karlshafen is a small spa town in northern Hesse, Germany, known for its baroque architecture and location at the confluence of the Weser and Diemel rivers.
-
E.
Gießen
Gießen is a mid-sized university city in central Germany known for its academic institutions and role as a regional administrative and cultural center.
- 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_69ab4b7e43c48190997b8fc8fb1663ab |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdd82a864819082bd1181a16d5208 |
completed | March 7, 2026, 8:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afc05adf908190bd16bbb6f8ea213a |
completed | March 10, 2026, 6:55 a.m. |
Created at: March 6, 2026, 9:57 p.m.