Triple
T22844512
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Le Pré Catelan |
E566176
|
entity |
| Predicate | MichelinRating |
P11848
|
FINISHED |
| Object | three Michelin stars |
—
|
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: three Michelin stars | Statement: [Le Pré Catelan, MichelinRating, three Michelin stars]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: MichelinRating Context triple: [Le Pré Catelan, MichelinRating, three Michelin stars]
-
A.
hasMichelinStar
chosen
Indicates that a restaurant or dining establishment has been awarded at least one Michelin star for its culinary quality.
-
B.
SkytraxRating
Indicates that an entity has been assigned a specific quality or performance rating according to the Skytrax airline and airport evaluation system.
-
C.
hasTourismRating
Indicates that an entity has been assigned a specific tourism-related quality or rating, reflecting its appeal or suitability for tourists.
-
D.
timeformRating
Indicates the performance quality or ability of an entity as measured by a specific time-based rating or scoring system.
-
E.
designedSpeedKmH
Indicates the maximum speed in kilometers per hour that something is intended or engineered to achieve under its design specifications.
- 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_69e245869e188190a196584f36e682da |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17e8726d4819095b4d999b4172ff7 |
completed | April 29, 2026, 3:44 a.m. |
| PD | Predicate disambiguation | batch_69eed2d117088190acbfe130d84f8627 |
completed | April 27, 2026, 3:06 a.m. |
Created at: April 17, 2026, 3:36 p.m.