Triple
T31632369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kizette de Lempicka |
E807202
|
entity |
| Predicate | frequentMuseOf |
P12879
|
FINISHED |
| Object | Tamara de Lempicka |
—
|
NE NERFINISHED |
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: Tamara de Lempicka | Statement: [Kizette de Lempicka, frequentMuseOf, Tamara de Lempicka]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frequentMuseOf Context triple: [Kizette de Lempicka, frequentMuseOf, Tamara de Lempicka]
-
A.
museOf
chosen
Indicates that one entity serves as the source of artistic or intellectual inspiration for another.
-
B.
literaryMuseOf
Indicates a relationship in which one entity serves as the creative inspiration or muse for another entity’s literary work.
-
C.
frequentlyUsedBy
Indicates that something is regularly or commonly utilized by a particular entity.
-
D.
favoritePoet
Indicates that one entity is the poet whom another entity prefers above all other poets.
-
E.
isOftenRecitedFor
Indicates that something (such as a text, phrase, or piece of music) is frequently spoken, chanted, or performed for a particular purpose, audience, or occasion.
- 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_69f348d892948190915f8facacb9568c |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6bbbef7a88190b0affdec1d41c1e0 |
completed | May 3, 2026, 3:06 a.m. |
| PD | Predicate disambiguation | batch_69f6ba6cef208190bc5cd43d96127004 |
completed | May 3, 2026, 3:01 a.m. |
Created at: April 30, 2026, 10:45 p.m.