Triple
T34891132
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Le Grand Diplôme |
E1006288
|
entity |
| Predicate | combinesTrainingIn |
P181838
|
FINISHED |
| Object | cuisine |
—
|
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: cuisine | Statement: [Le Grand Diplôme, combinesTrainingIn, cuisine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: combinesTrainingIn Context triple: [Le Grand Diplôme, combinesTrainingIn, cuisine]
-
A.
trainingIn
Indicates that one entity is undergoing or receiving training within the context, program, or domain specified by another entity.
-
B.
commonlyTrainedWith
Indicates that two entities are typically trained, practiced, or learned together as part of the same routine, curriculum, or skill set.
-
C.
alsoTrains
Indicates that an entity, in addition to its primary role or activity, is involved in training another entity.
-
D.
providesTrainingFor
Indicates that one entity delivers or conducts training activities intended to develop the skills or knowledge of another entity.
-
E.
trainingUse
Indicates that something is used for training purposes, such as preparing, educating, or improving the skills or performance of an entity.
- F. None of above. chosen
Provenance (4 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_69f76dbedb288190afe5780710847410 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f782cf61948190b98185d961609554 |
completed | May 3, 2026, 5:15 p.m. |
| PD | Predicate disambiguation | batch_69f781020cc4819088c40cb8589504e4 |
completed | May 3, 2026, 5:08 p.m. |
| PDg | Predicate description generation | batch_69f782c848fc8190baea8c845ca9079f |
completed | May 3, 2026, 5:15 p.m. |
Created at: May 3, 2026, 4 p.m.