Triple
T8848440
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schorndorf |
E210568
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object |
Schlichten
Schlichten is a district or locality within the town of Schorndorf in the German state of Baden-Württemberg.
|
E761179
|
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: Schlichten | Statement: [Schorndorf, hasSubdivision, Schlichten]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schlichten Context triple: [Schorndorf, hasSubdivision, Schlichten]
-
A.
Schonungen
Schonungen is a municipality in the Lower Franconia region of Bavaria, Germany, situated near the city of Schweinfurt along the Main River.
-
B.
Schaal
Schaal is a surname most notably associated with American actress Wendy Schaal, known for her work in film and television voice acting.
-
C.
Hanzelijn
Hanzelijn is a Dutch railway line connecting Lelystad and Zwolle, designed to shorten travel times between the Randstad and the northern Netherlands and partially built for higher-speed services.
-
D.
Schmutter
The Schmutter is a river in Bavaria, Germany, known as a regional tributary that flows through the Swabian landscape before joining the Wertach.
-
E.
Schillig
Schillig is a small coastal resort village in northern Germany known for its sandy North Sea beaches and proximity to the Wadden Sea mudflats.
- 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: Schlichten Triple: [Schorndorf, hasSubdivision, Schlichten]
Generated description
Schlichten is a district or locality within the town of Schorndorf in the German state of Baden-Württemberg.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Schlichten Target entity description: Schlichten is a district or locality within the town of Schorndorf in the German state of Baden-Württemberg.
-
A.
Schonungen
Schonungen is a municipality in the Lower Franconia region of Bavaria, Germany, situated near the city of Schweinfurt along the Main River.
-
B.
Schaal
Schaal is a surname most notably associated with American actress Wendy Schaal, known for her work in film and television voice acting.
-
C.
Hanzelijn
Hanzelijn is a Dutch railway line connecting Lelystad and Zwolle, designed to shorten travel times between the Randstad and the northern Netherlands and partially built for higher-speed services.
-
D.
Schmutter
The Schmutter is a river in Bavaria, Germany, known as a regional tributary that flows through the Swabian landscape before joining the Wertach.
-
E.
Schillig
Schillig is a small coastal resort village in northern Germany known for its sandy North Sea beaches and proximity to the Wadden Sea mudflats.
- 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_69ca838967bc8190b46c3c80a2887ea4 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc60aa6db0819097c3257499200afc |
completed | April 1, 2026, 12:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf89c6788881908d6f5c49434b556d |
completed | April 3, 2026, 9:35 a.m. |
| NEDg | Description generation | batch_69cf8ab7da348190b423f0768fe9dc1a |
completed | April 3, 2026, 9:39 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cf8bd252a4819098891bbb67baf897 |
completed | April 3, 2026, 9:43 a.m. |
Created at: March 30, 2026, 6:49 p.m.