Triple
T14463951
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madame Cézanne in a Yellow Chair |
E358656
|
entity |
| Predicate | portraysExpression |
P4750
|
FINISHED |
| Object | reserved demeanor |
—
|
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: reserved demeanor | Statement: [Madame Cézanne in a Yellow Chair, portraysExpression, reserved demeanor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysExpression Context triple: [Madame Cézanne in a Yellow Chair, portraysExpression, reserved demeanor]
-
A.
expresses
chosen
Indicates that one entity conveys, communicates, or articulates a thought, feeling, or idea through another medium or form.
-
B.
portraysPersonAs
Indicates that one entity represents, depicts, or characterizes another person in a particular way or role.
-
C.
portraysActivity
Indicates that one entity visually or narratively represents another entity engaged in a particular activity.
-
D.
portraysActorAs
Indicates that one entity depicts or represents an actor in a particular role, character, or manner.
-
E.
portraysState
Indicates that one entity visually or symbolically represents or depicts the condition, status, or situation of another entity.
- 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_69d827966698819082e140837737501d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de91ad67bc81908ecdaa7262f6dc55 |
completed | April 14, 2026, 7:12 p.m. |
| PD | Predicate disambiguation | batch_69de5c42bd3c81909a62acf30cc24d1e |
completed | April 14, 2026, 3:24 p.m. |
Created at: April 10, 2026, 1:19 a.m.