Triple
T5301134
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ortenaukreis |
E119983
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object |
Ettenheim
Ettenheim is a historic small town in southwestern Germany’s Baden-Württemberg region, known for its well-preserved old town and location near the Black Forest.
|
E527146
|
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: Ettenheim | Statement: [Ortenaukreis, containsTown, Ettenheim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ettenheim Context triple: [Ortenaukreis, containsTown, Ettenheim]
-
A.
Entzheim
Entzheim is a commune in northeastern France, near Strasbourg, best known for hosting Strasbourg Airport.
-
B.
Emsbach
Emsbach is a small river in Germany that flows through Hesse before joining the Lahn.
-
C.
Marlenheim
Marlenheim is a commune in northeastern France’s Alsace region, known as a historic wine-producing village and gateway to the area’s renowned vineyards and scenic countryside.
-
D.
Hettstadt
Hettstadt is a small municipality in the Würzburg district of Bavaria, Germany, known for its rural character and proximity to the city of Würzburg.
-
E.
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.
- 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: Ettenheim Triple: [Ortenaukreis, containsTown, Ettenheim]
Generated description
Ettenheim is a historic small town in southwestern Germany’s Baden-Württemberg region, known for its well-preserved old town and location near the Black Forest.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ettenheim Target entity description: Ettenheim is a historic small town in southwestern Germany’s Baden-Württemberg region, known for its well-preserved old town and location near the Black Forest.
-
A.
Entzheim
Entzheim is a commune in northeastern France, near Strasbourg, best known for hosting Strasbourg Airport.
-
B.
Emsbach
Emsbach is a small river in Germany that flows through Hesse before joining the Lahn.
-
C.
Marlenheim
Marlenheim is a commune in northeastern France’s Alsace region, known as a historic wine-producing village and gateway to the area’s renowned vineyards and scenic countryside.
-
D.
Hettstadt
Hettstadt is a small municipality in the Würzburg district of Bavaria, Germany, known for its rural character and proximity to the city of Würzburg.
-
E.
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.
- 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_69bd44704be88190acdb2ac481b0ff55 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd8509f67c8190b2f82a8370301a59 |
completed | March 20, 2026, 5:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bfd7104f008190b2bcacc8d071277c |
completed | March 22, 2026, 11:48 a.m. |
| NEDg | Description generation | batch_69bfd7b938d48190b81a6a0d039b5fa5 |
completed | March 22, 2026, 11:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69bfd84a09208190ae6eea7334fd3fe1 |
completed | March 22, 2026, 11:53 a.m. |
Created at: March 20, 2026, 1:53 p.m.