Triple
T14244654
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rüdesheim am Rhein |
E353100
|
entity |
| Predicate | hasCityPart |
P12399
|
FINISHED |
| Object | Assmannshausen |
E826743
|
NE FINISHED |
How this triple was built (2 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: Assmannshausen | Statement: [Rüdesheim am Rhein, hasCityPart, Assmannshausen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Assmannshausen Context triple: [Rüdesheim am Rhein, hasCityPart, Assmannshausen]
-
A.
Assmannshausen
chosen
Assmannshausen is a renowned wine-producing village in Germany’s Rheingau region, particularly famous for its red wines made from Spätburgunder (Pinot Noir).
-
B.
Helmarshausen
Helmarshausen is a historic district of the spa town Bad Karlshafen in northern Hesse, Germany, known for its medieval heritage and former Benedictine monastery.
-
C.
Weipertshausen
Weipertshausen is a small locality that forms part of the municipality of Münsing in Bavaria, Germany.
-
D.
Balzhausen
Balzhausen is a small municipality in the Bavarian region of Swabia in southern Germany.
-
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.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69fd3256026c8190814dd8ccd79c4b53 |
completed | May 8, 2026, 12:46 a.m. |
Created at: April 10, 2026, 1:08 a.m.