Triple
T37704268
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marchal |
E939157
|
entity |
| Predicate | awardedMichelinStar |
P151629
|
FINISHED |
| Object | 2014 |
—
|
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: 2014 | Statement: [Marchal, awardedMichelinStar, 2014]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: awardedMichelinStar Context triple: [Marchal, awardedMichelinStar, 2014]
-
A.
hasMichelinStar
Indicates that a restaurant or dining establishment has been awarded at least one Michelin star for its culinary quality.
-
B.
hasMichelinStarCount
Indicates the number of Michelin stars that have been awarded to a given entity, typically a restaurant or chef.
-
C.
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.
-
D.
secondMichelinStarYear
Indicates the year in which an entity (typically a restaurant or chef) was awarded its second Michelin star.
-
E.
firstMichelinStarYear
chosen
Indicates the year in which an entity (typically a restaurant or chef) received its first Michelin star.
- 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_69f76edb49dc8190b951dce9ce6ef789 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fdf5d05cc481909ec9e1b1f0784279 |
completed | May 8, 2026, 2:40 p.m. |
| PD | Predicate disambiguation | batch_69fdf0cdd6948190838864ab3120dfa6 |
completed | May 8, 2026, 2:18 p.m. |
Created at: May 3, 2026, 4:18 p.m.