Triple
T29095253
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Panther blue jersey |
E734978
|
entity |
| Predicate | sleeveDesign |
P110046
|
FINISHED |
| Object | features Panthers logo on sleeves |
—
|
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: features Panthers logo on sleeves | Statement: [Panther blue jersey, sleeveDesign, features Panthers logo on sleeves]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sleeveDesign Context triple: [Panther blue jersey, sleeveDesign, features Panthers logo on sleeves]
-
A.
hasTypicalSleeveStyle
Indicates the usual or characteristic sleeve design associated with an item, such as a garment or uniform.
-
B.
clothingFeature
chosen
Indicates that one entity has a specific clothing-related attribute, detail, or characteristic associated with it.
-
C.
coatCharacteristic
Indicates that one entity has a particular property, feature, or quality that characterizes its outer covering or surface.
-
D.
garmentType
Indicates the specific kind or category of garment associated with an entity.
-
E.
hasDesign
Indicates that one entity possesses, embodies, or is characterized by a particular design associated with 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_69f05b0ed66481908f2e864fa550d2f1 |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69f6618069888190aa87dd09a751a2c2 |
completed | May 2, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69f65c2376a08190be5215171e908e69 |
completed | May 2, 2026, 8:18 p.m. |
Created at: April 28, 2026, 11:08 a.m.