Triple
T19391588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ostalbkreis |
E485080
|
entity |
| Predicate | containsMunicipality |
P852
|
FINISHED |
| Object |
Wäschenbeuren
Wäschenbeuren is a small municipality in the German state of Baden-Württemberg, known for its rural character and location in the Swabian region.
|
E1373579
|
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: Wäschenbeuren | Statement: [Ostalbkreis, containsMunicipality, Wäschenbeuren]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wäschenbeuren Context triple: [Ostalbkreis, containsMunicipality, Wäschenbeuren]
-
A.
Tuchlauben
Tuchlauben is a historic street in Vienna’s city center, known for its upscale shops and proximity to major landmarks in the old town.
-
B.
Lorchhausen
Lorchhausen is a small district of the town of Lorch in the Rheingau region of Hesse, Germany, known for its winegrowing and scenic location along the Rhine River.
-
C.
La Meinau
La Meinau is a football stadium in Strasbourg, France, best known as the long-time home ground of RC Strasbourg Alsace.
-
D.
Dinkelscherben
Dinkelscherben is a municipality in the Swabian region of Bavaria in southern Germany.
-
E.
Bad Waldliesborn
Bad Waldliesborn is a spa village in the German region of Westphalia, known for its therapeutic mineral springs and health tourism.
- 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: Wäschenbeuren Triple: [Ostalbkreis, containsMunicipality, Wäschenbeuren]
Generated description
Wäschenbeuren is a small municipality in the German state of Baden-Württemberg, known for its rural character and location in the Swabian region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Wäschenbeuren Target entity description: Wäschenbeuren is a small municipality in the German state of Baden-Württemberg, known for its rural character and location in the Swabian region.
-
A.
Tuchlauben
Tuchlauben is a historic street in Vienna’s city center, known for its upscale shops and proximity to major landmarks in the old town.
-
B.
Lorchhausen
Lorchhausen is a small district of the town of Lorch in the Rheingau region of Hesse, Germany, known for its winegrowing and scenic location along the Rhine River.
-
C.
La Meinau
La Meinau is a football stadium in Strasbourg, France, best known as the long-time home ground of RC Strasbourg Alsace.
-
D.
Dinkelscherben
Dinkelscherben is a municipality in the Swabian region of Bavaria in southern Germany.
-
E.
Bad Waldliesborn
Bad Waldliesborn is a spa village in the German region of Westphalia, known for its therapeutic mineral springs and health tourism.
- 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_6a072b8951648190a7ea05504c3cd9c1 |
completed | May 15, 2026, 2:19 p.m. |
| NEDg | Description generation | batch_6a072e2e59388190ac5a6a9479555154 |
completed | May 15, 2026, 2:31 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a072eb872b88190904f9947228066fb |
completed | May 15, 2026, 2:33 p.m. |
Created at: April 10, 2026, 1:36 p.m.