Triple
T14244655
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rüdesheim am Rhein |
E353100
|
entity |
| Predicate | hasCityPart |
P12399
|
FINISHED |
| Object |
Aulhausen
Aulhausen is a district of the town Rüdesheim am Rhein in the Rheingau region of Hesse, Germany, known for its scenic vineyards and rural character.
|
E1152246
|
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: Aulhausen | Statement: [Rüdesheim am Rhein, hasCityPart, Aulhausen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aulhausen Context triple: [Rüdesheim am Rhein, hasCityPart, Aulhausen]
-
A.
Aulendorf
Aulendorf is a small town in the Upper Swabia region of southern Germany, known for its historic castle and spa facilities.
-
B.
Balzhausen
Balzhausen is a small municipality in the Bavarian region of Swabia in southern Germany.
-
C.
Aidhausen
Aidhausen is a small municipality in the Lower Franconia region of Bavaria, Germany.
-
D.
Ziegenhain
Ziegenhain is a historic town in the German state of Hesse, known for its medieval fortifications and role in regional conflicts.
-
E.
Ochsenhausen
Ochsenhausen is a small historic town in the German state of Baden-Württemberg, best known for its former Benedictine monastery, Ochsenhausen Abbey.
- 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: Aulhausen Triple: [Rüdesheim am Rhein, hasCityPart, Aulhausen]
Generated description
Aulhausen is a district of the town Rüdesheim am Rhein in the Rheingau region of Hesse, Germany, known for its scenic vineyards and rural character.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Aulhausen Target entity description: Aulhausen is a district of the town Rüdesheim am Rhein in the Rheingau region of Hesse, Germany, known for its scenic vineyards and rural character.
-
A.
Aulendorf
Aulendorf is a small town in the Upper Swabia region of southern Germany, known for its historic castle and spa facilities.
-
B.
Balzhausen
Balzhausen is a small municipality in the Bavarian region of Swabia in southern Germany.
-
C.
Aidhausen
Aidhausen is a small municipality in the Lower Franconia region of Bavaria, Germany.
-
D.
Ziegenhain
Ziegenhain is a historic town in the German state of Hesse, known for its medieval fortifications and role in regional conflicts.
-
E.
Ochsenhausen
Ochsenhausen is a small historic town in the German state of Baden-Württemberg, best known for its former Benedictine monastery, Ochsenhausen Abbey.
- 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_69d8278adc7c8190a9218d69bce3c4e6 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de6245d6a481909ef665748cd4d64c |
completed | April 14, 2026, 3:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff01d196d88190a8fa54468b2de1bb |
completed | May 9, 2026, 9:43 a.m. |
| NEDg | Description generation | batch_69ff02c10b648190b1e2e04aa0c2596d |
completed | May 9, 2026, 9:47 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff066e367c8190a0720fe636355ff8 |
completed | May 9, 2026, 10:03 a.m. |
Created at: April 10, 2026, 1:08 a.m.