Triple
T18420696
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bold as Love |
E442012
|
entity |
| Predicate | hasColorMetaphor |
P81126
|
FINISHED |
| Object | compares emotions to colors |
—
|
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: compares emotions to colors | Statement: [Bold as Love, hasColorMetaphor, compares emotions to colors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasColorMetaphor Context triple: [Bold as Love, hasColorMetaphor, compares emotions to colors]
-
A.
colorHasMeaning
Indicates that a particular color is associated with or conveys a specific meaning, symbolism, or significance.
-
B.
hasMetaphoricalForm
chosen
Indicates that one entity is expressed, represented, or understood through a metaphorical form or figurative expression involving another entity.
-
C.
hasMetaphoricalContent
Indicates that something contains or expresses meaning through metaphorical, rather than purely literal, content.
-
D.
visualMetaphor
Indicates a relationship where one entity conceptually represents or explains another through a visual analogy or symbolic imagery.
-
E.
colors
Indicates that one entity assigns, describes, or provides the color or colors of 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_69d8b9eb8a508190a942fd75ebd8b1dc |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e51a2be6bc8190b2812f77ff4cc960 |
completed | April 19, 2026, 6:08 p.m. |
| PD | Predicate disambiguation | batch_69e469bf7f74819096a01173493412c2 |
completed | April 19, 2026, 5:35 a.m. |
Created at: April 10, 2026, 10:47 a.m.