Triple
T28331417
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Waltzes |
E717546
|
entity |
| Predicate | emotionalRange |
P153193
|
FINISHED |
| Object | from light and graceful to melancholic |
—
|
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: from light and graceful to melancholic | Statement: [Waltzes, emotionalRange, from light and graceful to melancholic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: emotionalRange Context triple: [Waltzes, emotionalRange, from light and graceful to melancholic]
-
A.
emotionalScope
chosen
Indicates the range or extent of emotions involved or affected within a given relationship or situation.
-
B.
emotionalDynamic
Indicates how emotions, moods, or affective states change, interact, or influence each other between entities over time.
-
C.
emotionalTrait
Indicates that an entity possesses a particular emotional characteristic, disposition, or affective quality.
-
D.
hasEmotionalIntensity
Indicates that an emotion, experience, or expression is characterized by a particular degree or strength of emotional impact.
-
E.
emotionDomain
Indicates the general emotional category or type to which a specific emotion belongs (e.g., grouping emotions into broader domains like joy, anger, or fear).
- 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_69eff6e9a57c8190a69c2c74b5d72119 |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69f64e37f7fc819083809149b6661e3c |
completed | May 2, 2026, 7:19 p.m. |
| PD | Predicate disambiguation | batch_69f64caede108190a35cc7cbfead866f |
completed | May 2, 2026, 7:12 p.m. |
Created at: April 28, 2026, 12:32 a.m.