Triple
T13840921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Le Crazy Horse de Paris |
E332651
|
entity |
| Predicate | dressCodeForGuests |
P2738
|
FINISHED |
| Object | smart casual |
—
|
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: smart casual | Statement: [Le Crazy Horse de Paris, dressCodeForGuests, smart casual]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dressCodeForGuests Context triple: [Le Crazy Horse de Paris, dressCodeForGuests, smart casual]
-
A.
hasDressCode
chosen
Indicates that a specified entity enforces or is associated with a particular set of rules governing appropriate clothing or attire.
-
B.
usualAttire
Indicates the type of clothing an entity typically wears in ordinary or characteristic situations.
-
C.
usesDressing
Indicates that one entity applies or employs a particular dressing (such as a sauce, covering, or treatment) in relation to another entity or context.
-
D.
guestAtWedding
Indicates that a person is attending or has attended a particular wedding as a guest.
-
E.
entryOccasion
Indicates the circumstance, event, or reason that prompts or justifies an entity’s entry or initiation.
- 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_69d81c5ba13c8190839315f54768acfd |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02ae5e4c8190ad85ad2968bc71b2 |
completed | April 14, 2026, 9:02 a.m. |
| PD | Predicate disambiguation | batch_69dbc86668e08190ba9135d1c3f38d35 |
completed | April 12, 2026, 4:29 p.m. |
Created at: April 9, 2026, 10:13 p.m.