Triple
T9910436
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | US Città di Palermo |
E185126
|
entity |
| Predicate | notableColorCombination |
P60
|
FINISHED |
| Object | pink shirts and black shorts |
—
|
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: pink shirts and black shorts | Statement: [US Città di Palermo, notableColorCombination, pink shirts and black shorts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableColorCombination Context triple: [US Città di Palermo, notableColorCombination, pink shirts and black shorts]
-
A.
notableColor
Indicates that an entity is characteristically or prominently associated with a particular color.
-
B.
colors
chosen
Indicates that one entity assigns, describes, or provides the color or colors of another entity.
-
C.
associatedColour
Indicates that one entity is linked to another as its characteristic or representative colour.
-
D.
notableOutfit
Indicates that an entity is known for or associated with wearing a particular outfit or style of clothing.
-
E.
colorOftenUsed
Indicates that a particular color is frequently used or commonly applied in relation to something.
- 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_69ca8296165881908ca4750701af1f29 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cdb512a26881908eb72a21ffb1efef |
completed | April 2, 2026, 12:15 a.m. |
| PD | Predicate disambiguation | batch_69cd1d8c584081908b73de75eb18e438 |
completed | April 1, 2026, 1:28 p.m. |
Created at: March 30, 2026, 8:41 p.m.