Triple
T19391570
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ostalbkreis |
E485080
|
entity |
| Predicate | containsMunicipality |
P852
|
FINISHED |
| Object |
Essingen
Essingen is a municipality in the Ostalbkreis district of the state of Baden-Württemberg in southern Germany.
|
E1387029
|
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: Essingen | Statement: [Ostalbkreis, containsMunicipality, Essingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Essingen Context triple: [Ostalbkreis, containsMunicipality, Essingen]
-
A.
Wechingen
Wechingen is a small rural municipality in the Bavarian region of southern Germany.
-
B.
Ellingen
Ellingen is a historic market town in Bavaria, Germany, known for its well-preserved baroque architecture and the former residence of the Teutonic Order.
-
C.
Effingen
Effingen is a small Swiss village and former municipality in the canton of Aargau, known for its rural character and location near the Jura Mountains.
-
D.
Gechingen
Gechingen is a small municipality in the German state of Baden-Württemberg, situated in the northern Black Forest region.
-
E.
Eppingen
Eppingen is a historic town in the Kraichgau region of Baden-Württemberg, Germany, known for its well-preserved half-timbered houses and medieval architecture.
- 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: Essingen Triple: [Ostalbkreis, containsMunicipality, Essingen]
Generated description
Essingen is a municipality in the Ostalbkreis district of the state of Baden-Württemberg in southern Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Essingen Target entity description: Essingen is a municipality in the Ostalbkreis district of the state of Baden-Württemberg in southern Germany.
-
A.
Wechingen
Wechingen is a small rural municipality in the Bavarian region of southern Germany.
-
B.
Ellingen
Ellingen is a historic market town in Bavaria, Germany, known for its well-preserved baroque architecture and the former residence of the Teutonic Order.
-
C.
Effingen
Effingen is a small Swiss village and former municipality in the canton of Aargau, known for its rural character and location near the Jura Mountains.
-
D.
Gechingen
Gechingen is a small municipality in the German state of Baden-Württemberg, situated in the northern Black Forest region.
-
E.
Eppingen
Eppingen is a historic town in the Kraichgau region of Baden-Württemberg, Germany, known for its well-preserved half-timbered houses and medieval architecture.
- 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_69d8e8d460d88190abf0591c5c9d2b0c |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e61b45caec81909dafdf66b361effd |
completed | April 20, 2026, 12:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a07719a9afc81908d69dbdbdd90fb1e |
completed | May 15, 2026, 7:18 p.m. |
| NEDg | Description generation | batch_6a0777edb52c8190b17ca42cc690cdcd |
completed | May 15, 2026, 7:45 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0778acf3888190a292aa6775171c90 |
completed | May 15, 2026, 7:49 p.m. |
Created at: April 10, 2026, 1:36 p.m.