Triple
T724343
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michaelis-Kirchweih |
E14689
|
entity |
| Predicate | hasFoodSpeciality |
P17971
|
FINISHED |
| Object | Fränkische Bratwürste |
—
|
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: Fränkische Bratwürste | Statement: [Michaelis-Kirchweih, hasFoodSpeciality, Fränkische Bratwürste]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFoodSpeciality Context triple: [Michaelis-Kirchweih, hasFoodSpeciality, Fränkische Bratwürste]
-
A.
hasSpecialtyFood
chosen
Indicates that an entity offers, serves, or is associated with a particular type of specialty food.
-
B.
hasCuisineItem
Indicates that a particular cuisine includes, features, or is associated with a specific food item.
-
C.
hasStapleFood
Indicates that an entity’s primary or regularly consumed basic food item is another specified entity.
-
D.
cuisineFeature
Indicates a characteristic, quality, or notable aspect that describes or distinguishes a particular cuisine.
-
E.
hasSpecialMeal
Indicates that an entity provides, is assigned, or is associated with a designated special meal option.
- 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_69a4934c753c81909b309027e48b9b3a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a5a6ab508190b70a05a9d77829a5 |
completed | March 1, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69a4a4f700cc81908c6de3eedf68433c |
completed | March 1, 2026, 8:43 p.m. |
Created at: March 1, 2026, 7:37 p.m.