Triple
T19208364
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Portrait of a Man |
E480293
|
entity |
| Predicate | sitterPose |
P55537
|
FINISHED |
| Object | three-quarter view |
—
|
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: three-quarter view | Statement: [Portrait of a Man, sitterPose, three-quarter view]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sitterPose Context triple: [Portrait of a Man, sitterPose, three-quarter view]
-
A.
sitterIn
Indicates that one entity is acting as a sitter (e.g., babysitter, pet sitter, house sitter) for another entity or at a particular place.
-
B.
sitter
Indicates that one entity is serving as a caretaker or guardian, typically watching over or looking after another entity.
-
C.
sitterOf
Indicates that one entity serves as a caretaker or babysitter responsible for looking after another entity.
-
D.
positionDuringSitting
Indicates the spatial position or posture an entity has specifically while it is in a sitting state.
-
E.
hasSeatingPose
chosen
Indicates that an entity is in a seated posture or arrangement, specifying how it is positioned while sitting.
- 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_69d8e8cb8c348190b52075823911c869 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5f9a000188190afb762ea24bc3deb |
completed | April 20, 2026, 10:02 a.m. |
| PD | Predicate disambiguation | batch_69e4dcf22b3c8190bee02e3af946e114 |
completed | April 19, 2026, 1:47 p.m. |
Created at: April 10, 2026, 1:20 p.m.