Triple
T1663075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cologne |
E35950
|
entity |
| Predicate | hasLocalDialect |
P1762
|
FINISHED |
| Object |
Kölsch
Kölsch is a Ripuarian dialect of the German language traditionally spoken in and around the city of Cologne.
|
E187426
|
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: Kölsch | Statement: [Cologne, hasLocalDialect, Kölsch]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kölsch Context triple: [Cologne, hasLocalDialect, Kölsch]
-
A.
Grimbergen
Grimbergen is a municipality in the Flemish Brabant province of Belgium, known for its historic Norbertine abbey and the Grimbergen abbey beer.
-
B.
Heineken lager beer
Heineken lager beer is a globally recognized pale lager known for its distinctive green bottle, red star logo, and crisp, mildly bitter taste.
-
C.
Amstel
The Amstel is a river in the Netherlands that flows through Amsterdam and has given its name to the city and a well-known Dutch beer brand.
-
D.
Amsterdam Amstel
Amsterdam Amstel is a major railway and metro station in Amsterdam that serves as an important transport hub connecting regional and local lines.
-
E.
Grolsch
Grolsch is a Dutch brewery best known for its distinctive swing-top bottled beers and long-standing presence in the international beer market.
- 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: Kölsch Triple: [Cologne, hasLocalDialect, Kölsch]
Generated description
Kölsch is a Ripuarian dialect of the German language traditionally spoken in and around the city of Cologne.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kölsch Target entity description: Kölsch is a Ripuarian dialect of the German language traditionally spoken in and around the city of Cologne.
-
A.
Grimbergen
Grimbergen is a municipality in the Flemish Brabant province of Belgium, known for its historic Norbertine abbey and the Grimbergen abbey beer.
-
B.
Heineken lager beer
Heineken lager beer is a globally recognized pale lager known for its distinctive green bottle, red star logo, and crisp, mildly bitter taste.
-
C.
Amstel
The Amstel is a river in the Netherlands that flows through Amsterdam and has given its name to the city and a well-known Dutch beer brand.
-
D.
Amsterdam Amstel
Amsterdam Amstel is a major railway and metro station in Amsterdam that serves as an important transport hub connecting regional and local lines.
-
E.
Grolsch
Grolsch is a Dutch brewery best known for its distinctive swing-top bottled beers and long-standing presence in the international beer market.
- 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_69a88606aa808190aa0b421b4271f220 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a90ab5d1a08190a3325ff203b573fb |
completed | March 5, 2026, 4:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad682d968081909494920f3a7ea3af |
completed | March 8, 2026, 12:14 p.m. |
| NEDg | Description generation | batch_69ad69017e448190b337337431c6f797 |
completed | March 8, 2026, 12:18 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad6966c3108190bf2519f698dd3903 |
completed | March 8, 2026, 12:19 p.m. |
Created at: March 4, 2026, 7:29 p.m.