Triple
T10047777
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sara Sampaio |
E207659
|
entity |
| Predicate | hasModeledCategory |
P91822
|
FINISHED |
| Object | lingerie |
—
|
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: lingerie | Statement: [Sara Sampaio, hasModeledCategory, lingerie]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasModeledCategory Context triple: [Sara Sampaio, hasModeledCategory, lingerie]
-
A.
hasCategorySystem
Indicates that an entity is associated with or organized according to a particular categorization system.
-
B.
hasCategoryOn
Indicates that something is assigned to or associated with a specific category within a given context or scope.
-
C.
hasCategories
Indicates that an entity is associated with one or more categories that classify or group it.
-
D.
hasModelledFor
Indicates that one entity has served as a model for another entity, typically in a professional or representational context such as art, photography, or fashion.
-
E.
hasCategoryCount
Indicates the number of distinct categories associated with a given entity.
- F. None of above. chosen
Provenance (4 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_69ca835ad0608190b7c80b292da004f5 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cdcf664dd881908786fcd802bf10da |
completed | April 2, 2026, 2:07 a.m. |
| PD | Predicate disambiguation | batch_69cd4b8d2280819089de27e57babd1f3 |
completed | April 1, 2026, 4:45 p.m. |
| PDg | Predicate description generation | batch_69cd4f8d9b888190b8067bd916dae773 |
completed | April 1, 2026, 5:02 p.m. |
Created at: March 30, 2026, 8:56 p.m.