Triple
T8040283
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old Town (Altstadt) |
E187418
|
entity |
| Predicate | localSpecialty |
P17971
|
FINISHED |
| Object | Kölsch beer |
—
|
LITERAL 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: Kölsch beer | Statement: [Old Town (Altstadt), localSpecialty, Kölsch beer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: localSpecialty Context triple: [Old Town (Altstadt), localSpecialty, Kölsch beer]
-
A.
hasSpecialtyFood
chosen
Indicates that an entity offers, serves, or is associated with a particular type of specialty food.
-
B.
cuisineFeature
Indicates a characteristic, quality, or notable aspect that describes or distinguishes a particular cuisine.
-
C.
alsoEats
Indicates that an entity consumes something in addition to another item or items it already eats.
-
D.
servesMostly
Indicates that one entity primarily functions to serve, support, or cater to another entity, more than to any other.
-
E.
regionOfCulinaryImportance
Indicates that a location is recognized for its significant culinary relevance, such as notable food traditions, specialties, or gastronomic culture.
- F. None of above.
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_69ca82b00cb48190b59a300f70e97bd7 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3f1d62c48190bf4a6cd17517c5dc |
completed | March 31, 2026, 3:27 a.m. |
| PD | Predicate disambiguation | batch_69cb049688208190b32088bd2c5930bc |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:23 p.m.