Triple
T10607872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | little black dress |
E275922
|
entity |
| Predicate | hasTypicalSleeveStyle |
P94904
|
FINISHED |
| Object | sleeveless |
—
|
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: sleeveless | Statement: [little black dress, hasTypicalSleeveStyle, sleeveless]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalSleeveStyle Context triple: [little black dress, hasTypicalSleeveStyle, sleeveless]
-
A.
coatCharacteristic
Indicates that one entity has a particular property, feature, or quality that characterizes its outer covering or surface.
-
B.
garmentType
Indicates the specific kind or category of garment associated with an entity.
-
C.
hasShoulderButtons
Indicates that an object, typically a device or controller, includes buttons positioned on its shoulders or top side edges.
-
D.
hasGarment
Indicates that one entity possesses, wears, or is associated with a particular garment.
-
E.
shirtNumberType
Indicates the type or category of a shirt number assigned to an entity (for example, a player’s jersey number type).
- 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_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. |
| PDg | Predicate description generation | batch_69d6df463ea8819091d6683e476b4f21 |
completed | April 8, 2026, 11:05 p.m. |
Created at: April 8, 2026, 7:32 p.m.