Triple
T24332068
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hôtel Martinez |
E613272
|
entity |
| Predicate | LaPalmeDorMichelinStars |
P151628
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Hôtel Martinez, LaPalmeDorMichelinStars, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: LaPalmeDorMichelinStars Context triple: [Hôtel Martinez, LaPalmeDorMichelinStars, 2]
-
A.
hasMichelinStar
Indicates that a restaurant or dining establishment has been awarded at least one Michelin star for its culinary quality.
-
B.
hasMichelinStarCount
chosen
Indicates the number of Michelin stars that have been awarded to a given entity, typically a restaurant or chef.
-
C.
palmeDorSection
Indicates that an entity belongs to the section or category associated with the Palme d'Or award (e.g., the main competition section at the Cannes Film Festival).
-
D.
secondMichelinStarYear
Indicates the year in which an entity (typically a restaurant or chef) was awarded its second Michelin star.
-
E.
awardedThreeMichelinStarsSince
Indicates that an entity (typically a restaurant) has been granted three Michelin stars starting from a specified time and has held that top rating since then.
- 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_69e2d7db6d5c819091194918157a7c1f |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f292f23db08190b701bb13aef7f10d |
completed | April 29, 2026, 11:23 p.m. |
| PD | Predicate disambiguation | batch_69f287ad30048190b3ad3613486f277f |
completed | April 29, 2026, 10:35 p.m. |
Created at: April 18, 2026, 1:55 a.m.