Triple
T19446901
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eugendorf |
E486501
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object |
Schwaighofen
Schwaighofen is a locality within the municipality of Eugendorf in the Austrian state of Salzburg.
|
E1387835
|
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: Schwaighofen | Statement: [Eugendorf, hasSubdivision, Schwaighofen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schwaighofen Context triple: [Eugendorf, hasSubdivision, Schwaighofen]
-
A.
Ellhofen
Ellhofen is a small municipality in the German state of Baden-Württemberg, situated in the Heilbronn region and known for its wine-growing landscape.
-
B.
Schwabniederhofen
Schwabniederhofen is a small municipality located in the Weilheim-Schongau district of Bavaria in southern Germany.
-
C.
Pfaffenweiler
Pfaffenweiler is a small municipality in southwestern Germany’s Baden-Württemberg region, situated near Freiburg im Breisgau and known for its winegrowing and picturesque Black Forest surroundings.
-
D.
Schwanstetten
Schwanstetten is a municipality in the Roth district of Bavaria, Germany, known for its residential character and proximity to the city of Nuremberg.
-
E.
Wehofen
Wehofen is a district of the Walsum area in the city of Duisburg in North Rhine-Westphalia, Germany.
- 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: Schwaighofen Triple: [Eugendorf, hasSubdivision, Schwaighofen]
Generated description
Schwaighofen is a locality within the municipality of Eugendorf in the Austrian state of Salzburg.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Schwaighofen Target entity description: Schwaighofen is a locality within the municipality of Eugendorf in the Austrian state of Salzburg.
-
A.
Ellhofen
Ellhofen is a small municipality in the German state of Baden-Württemberg, situated in the Heilbronn region and known for its wine-growing landscape.
-
B.
Schwabniederhofen
Schwabniederhofen is a small municipality located in the Weilheim-Schongau district of Bavaria in southern Germany.
-
C.
Pfaffenweiler
Pfaffenweiler is a small municipality in southwestern Germany’s Baden-Württemberg region, situated near Freiburg im Breisgau and known for its winegrowing and picturesque Black Forest surroundings.
-
D.
Schwanstetten
Schwanstetten is a municipality in the Roth district of Bavaria, Germany, known for its residential character and proximity to the city of Nuremberg.
-
E.
Wehofen
Wehofen is a district of the Walsum area in the city of Duisburg in North Rhine-Westphalia, Germany.
- 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_69d8e8d7ad488190a3373045029b0f3b |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6338a22608190bb31a1690ca0dab6 |
completed | April 20, 2026, 2:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a077ed1a7948190bcefd3c02c9a5bec |
completed | May 15, 2026, 8:15 p.m. |
| NEDg | Description generation | batch_6a077f87d96c8190a8d9ee82e84be1f2 |
completed | May 15, 2026, 8:18 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a07816f54408190b6458ed0d17f9b60 |
completed | May 15, 2026, 8:26 p.m. |
Created at: April 10, 2026, 1:38 p.m.