Triple
T38066840
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Washer Woman Arch |
E950491
|
entity |
| Predicate | hasSilhouetteResembling |
P40118
|
FINISHED |
| Object | a woman bent over a washtub |
—
|
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: a woman bent over a washtub | Statement: [Washer Woman Arch, hasSilhouetteResembling, a woman bent over a washtub]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSilhouetteResembling Context triple: [Washer Woman Arch, hasSilhouetteResembling, a woman bent over a washtub]
-
A.
hasSilhouetteShape
chosen
Indicates that one entity has the overall outline or contour shape specified or characterized by another entity.
-
B.
hasPhotographicAppearanceIn
Indicates that one entity appears in a photograph or photographic representation associated with another entity.
-
C.
resembles
Indicates that one entity is similar in appearance, form, or characteristics to another.
-
D.
hasDoppelganger
Indicates that an entity has a counterpart or double that closely resembles it, often in appearance, behavior, or role.
-
E.
hasCharacterAppearance
Indicates that a character appears or is visually represented within a given work, scene, or context.
- 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_69f76f01e63c819093b6012fc974f35a |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fe86cad5108190b0164b8bc6fc23ea |
completed | May 9, 2026, 12:58 a.m. |
| PD | Predicate disambiguation | batch_69fe83c0c9888190b6fc40c7f727b569 |
completed | May 9, 2026, 12:45 a.m. |
Created at: May 3, 2026, 4:21 p.m.