Triple
T9960351
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kunstmuseum Stuttgart |
E195550
|
entity |
| Predicate | hasRestaurantOrCafe |
P40355
|
FINISHED |
| Object | museum restaurant |
—
|
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: museum restaurant | Statement: [Kunstmuseum Stuttgart, hasRestaurantOrCafe, museum restaurant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRestaurantOrCafe Context triple: [Kunstmuseum Stuttgart, hasRestaurantOrCafe, museum restaurant]
-
A.
hasRestaurantsAndCafes
chosen
Indicates that the subject location contains or provides access to restaurants and cafés.
-
B.
hasRestaurant
Indicates that one entity possesses, operates, or contains a restaurant associated with it.
-
C.
hasRestaurantType
Indicates that an entity is associated with or classified as a particular type or category of restaurant.
-
D.
hasCafes
Indicates that one entity possesses, contains, or includes one or more cafes within it.
-
E.
hasLoungeOrCafe
Indicates that one entity provides or includes access to a lounge or café associated with another entity.
- 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_69ca82eaaa008190a54fa1a9f954b9ad |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb6d219c48190b2084b0eb07ae125 |
completed | April 2, 2026, 12:22 a.m. |
| PD | Predicate disambiguation | batch_69cd1d9ae19c819099fb3635e57c79be |
completed | April 1, 2026, 1:28 p.m. |
Created at: March 30, 2026, 8:46 p.m.