Triple
T14420093
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Portrait of Marie-Adélaïde of France as Diana |
E357559
|
entity |
| Predicate | portraysAgeCategory |
P13483
|
FINISHED |
| Object | young woman |
—
|
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: young woman | Statement: [Portrait of Marie-Adélaïde of France as Diana, portraysAgeCategory, young woman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysAgeCategory Context triple: [Portrait of Marie-Adélaïde of France as Diana, portraysAgeCategory, young woman]
-
A.
portraysAgeGroup
chosen
Indicates that one entity depicts or represents another entity as belonging to a particular age group.
-
B.
portraysFromAge
Indicates that one entity depicts another entity starting from a specified age of the depicted entity.
-
C.
portrayedByCharacterAgeApprox
Indicates that an entity is portrayed by a character whose age is approximately a specified value or age range.
-
D.
portrayedAsAdultBy
Indicates that one entity is depicted or represented as an adult by another entity (such as an artist, author, or creator).
-
E.
characterAgeDescriptor
Indicates how a character’s age is qualitatively described or categorized (e.g., young, middle-aged, elderly) rather than given as a specific number.
- 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_69d82793421c8190861eb0e673b085de |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de910eb354819089d5d5a46919eb49 |
completed | April 14, 2026, 7:10 p.m. |
| PD | Predicate disambiguation | batch_69de5c30467881908e770e3940295641 |
completed | April 14, 2026, 3:24 p.m. |
Created at: April 10, 2026, 1:18 a.m.