Triple
T21382655
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wiesloch |
E527400
|
entity |
| Predicate | hasDistrict |
P459
|
FINISHED |
| Object |
Baiertal
Baiertal is a village and district of the town of Wiesloch in the Rhine-Neckar region of Baden-Württemberg, Germany.
|
E1490067
|
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: Baiertal | Statement: [Wiesloch, hasDistrict, Baiertal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Baiertal Context triple: [Wiesloch, hasDistrict, Baiertal]
-
A.
Deggenhausertal
Deggenhausertal is a rural municipality in the Bodenseekreis district of Baden-Württemberg in southern Germany, known for its scenic valley landscape near Lake Constance.
-
B.
Sulztal
Sulztal is a mountain valley in the Stubai Alps of Tyrol, Austria, known for its alpine landscapes and hiking routes.
-
C.
Waldachtal
Waldachtal is a small municipality in the Black Forest region of southwestern Germany, known for its rural landscape and spa and tourism activities.
-
D.
Fischbachtal
Fischbachtal is a small municipality in southern Hesse, Germany, known for its rural landscape and the historic Lichtenberg Castle.
-
E.
Düsseltal
Düsseltal is a residential district of Düsseldorf, Germany, known for its leafy streets, affluent character, and mix of historic and modern urban development.
- 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: Baiertal Triple: [Wiesloch, hasDistrict, Baiertal]
Generated description
Baiertal is a village and district of the town of Wiesloch in the Rhine-Neckar region of Baden-Württemberg, Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Baiertal Target entity description: Baiertal is a village and district of the town of Wiesloch in the Rhine-Neckar region of Baden-Württemberg, Germany.
-
A.
Deggenhausertal
Deggenhausertal is a rural municipality in the Bodenseekreis district of Baden-Württemberg in southern Germany, known for its scenic valley landscape near Lake Constance.
-
B.
Sulztal
Sulztal is a mountain valley in the Stubai Alps of Tyrol, Austria, known for its alpine landscapes and hiking routes.
-
C.
Waldachtal
Waldachtal is a small municipality in the Black Forest region of southwestern Germany, known for its rural landscape and spa and tourism activities.
-
D.
Fischbachtal
Fischbachtal is a small municipality in southern Hesse, Germany, known for its rural landscape and the historic Lichtenberg Castle.
-
E.
Düsseltal
Düsseltal is a residential district of Düsseldorf, Germany, known for its leafy streets, affluent character, and mix of historic and modern urban development.
- 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_69e0b51f363c8190944000ab5523b02b |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b0d1fa1c8190b3374e0bb3a971fc |
completed | April 22, 2026, 11:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09eecc2a408190a923b8455e2617d7 |
completed | May 17, 2026, 4:37 p.m. |
| NEDg | Description generation | batch_6a09efc6a57c81908c72e794c16989d4 |
completed | May 17, 2026, 4:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09f07a090c8190aead09c3b06e0e34 |
completed | May 17, 2026, 4:44 p.m. |
Created at: April 16, 2026, 5:12 p.m.