Triple
T19034053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Portrait of a Young Woman (Frans Hals) |
E465818
|
entity |
| Predicate | hasAgeOfSitter |
P92820
|
FINISHED |
| Object | young adult |
—
|
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 adult | Statement: [Portrait of a Young Woman (Frans Hals), hasAgeOfSitter, young adult]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAgeOfSitter Context triple: [Portrait of a Young Woman (Frans Hals), hasAgeOfSitter, young adult]
-
A.
sitterOf
Indicates that one entity serves as a caretaker or babysitter responsible for looking after another entity.
-
B.
sitter
Indicates that one entity is serving as a caretaker or guardian, typically watching over or looking after another entity.
-
C.
sitterAgeAtTimeOfPortrait
chosen
Indicates the age of the sitter at the specific time when the portrait was created or captured.
-
D.
sitterStatus
Indicates the current role, availability, or engagement state of a sitter in relation to a sitting arrangement or service.
-
E.
sitterNationality
Indicates the national identity or citizenship of the person who is sitting for a portrait or being depicted.
- 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_69d8dd0359648190bc2a9202c5cf29d2 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d74295b88190b1c4621735a06223 |
completed | April 20, 2026, 7:35 a.m. |
| PD | Predicate disambiguation | batch_69e4a3001e388190aa6057266514e75a |
completed | April 19, 2026, 9:40 a.m. |
Created at: April 10, 2026, 12:02 p.m.