Triple
T5483564
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Rossa |
E123522
|
entity |
| Predicate | colorConnotation |
P46606
|
FINISHED |
| Object | red |
—
|
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: red | Statement: [La Rossa, colorConnotation, red]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: colorConnotation Context triple: [La Rossa, colorConnotation, red]
-
A.
colorHasMeaning
chosen
Indicates that a particular color is associated with or conveys a specific meaning, symbolism, or significance.
-
B.
colors
Indicates that one entity assigns, describes, or provides the color or colors of another entity.
-
C.
hasConnotation
Indicates that one entity carries an implied or associated meaning, tone, or emotional nuance in relation to another entity.
-
D.
starColorSymbolism
Indicates how the color of a star is associated with particular symbolic meanings or themes.
-
E.
socialConnotation
Indicates the commonly understood social meaning, implication, or value judgment that people associate with something within a given cultural or social context.
- 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_69bd4648883481909e9775d43300c5fa |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd93e5d0f08190a6cc9fc408b7c5bb |
completed | March 20, 2026, 6:37 p.m. |
| PD | Predicate disambiguation | batch_69bd91a73b148190a865243536a4fe76 |
completed | March 20, 2026, 6:27 p.m. |
Created at: March 20, 2026, 2:10 p.m.