Triple
T5027014
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Konstanz (district) |
E113199
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Orsingen-Nenzingen
Orsingen-Nenzingen is a small municipality in the state of Baden-Württemberg in southern Germany.
|
E487692
|
NE FINISHED |
How this triple was built (4 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: Orsingen-Nenzingen | Statement: [Konstanz (district), hasMunicipality, Orsingen-Nenzingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orsingen-Nenzingen Context triple: [Konstanz (district), hasMunicipality, Orsingen-Nenzingen]
-
A.
Oensingen
Oensingen is a Swiss municipality located in the canton of Solothurn, known as a regional transport hub near the Jura mountains.
-
B.
Münsingen
Münsingen is a Swiss municipality in the canton of Bern, known for its scenic location in the Aare valley between Bern and Thun.
-
C.
Gernsbach
Gernsbach is a historic town in southwestern Germany’s Black Forest region, known for its medieval old town and picturesque setting along the Murg River.
-
D.
Laichingen
Laichingen is a small town in the Alb-Donau district of Baden-Württemberg in southern Germany, known for its location on the Swabian Jura plateau and its historic textile industry.
-
E.
Weiningen
Weiningen is a small Swiss municipality in the canton of Zurich, located in the Limmat Valley near the city of Zurich.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Orsingen-Nenzingen Triple: [Konstanz (district), hasMunicipality, Orsingen-Nenzingen]
Generated description
Orsingen-Nenzingen is a small municipality in the state of Baden-Württemberg in southern Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Orsingen-Nenzingen Target entity description: Orsingen-Nenzingen is a small municipality in the state of Baden-Württemberg in southern Germany.
-
A.
Oensingen
Oensingen is a Swiss municipality located in the canton of Solothurn, known as a regional transport hub near the Jura mountains.
-
B.
Münsingen
Münsingen is a Swiss municipality in the canton of Bern, known for its scenic location in the Aare valley between Bern and Thun.
-
C.
Gernsbach
Gernsbach is a historic town in southwestern Germany’s Black Forest region, known for its medieval old town and picturesque setting along the Murg River.
-
D.
Laichingen
Laichingen is a small town in the Alb-Donau district of Baden-Württemberg in southern Germany, known for its location on the Swabian Jura plateau and its historic textile industry.
-
E.
Weiningen
Weiningen is a small Swiss municipality in the canton of Zurich, located in the Limmat Valley near the city of Zurich.
- F. None of above. chosen
Provenance (5 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_69bd443775e48190a646ffbfc4334723 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd738c3aac81908fb6a5c70c97a394 |
completed | March 20, 2026, 4:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be9c64db5c81909224c82ae9d9e0ab |
completed | March 21, 2026, 1:25 p.m. |
| NEDg | Description generation | batch_69be9ce7959081908b9ddb4c677c477c |
completed | March 21, 2026, 1:28 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69be9d7e00f88190b2d12e872fadc181 |
completed | March 21, 2026, 1:30 p.m. |
Created at: March 20, 2026, 1:36 p.m.