Triple
T10607866
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | little black dress |
E275922
|
entity |
| Predicate | hasTypicalSilhouette |
P40118
|
FINISHED |
| Object | sheath dress |
—
|
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: sheath dress | Statement: [little black dress, hasTypicalSilhouette, sheath dress]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalSilhouette Context triple: [little black dress, hasTypicalSilhouette, sheath dress]
-
A.
hasSilhouetteShape
chosen
Indicates that one entity has the overall outline or contour shape specified or characterized by another entity.
-
B.
typicalFigure
Indicates that one entity serves as a standard or representative example (a typical instance) of the other entity.
-
C.
hasDistinctiveShape
Indicates that an entity possesses a shape or form that is notably different from others and can be easily recognized or distinguished.
-
D.
introducedSilhouette
Indicates that one entity caused another entity to first appear or be presented in outline or partial form, rather than in full detail.
-
E.
hasTypicalCut
Indicates that one entity is characterized by or associated with a standard or typical type of cut 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_69d6aaf948d88190806cc3a8c47a3fb2 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d6df4c38c881908f69bb757b8e03f5 |
completed | April 8, 2026, 11:05 p.m. |
| PD | Predicate disambiguation | batch_69d6dd72c1288190adbb5e79e94c044a |
completed | April 8, 2026, 10:57 p.m. |
Created at: April 8, 2026, 7:32 p.m.