Triple
T38358214
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dumbo's mother |
E1046392
|
entity |
| Predicate | emotionalToneOfScenes |
P171341
|
FINISHED |
| Object | poignant |
—
|
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: poignant | Statement: [Dumbo's mother, emotionalToneOfScenes, poignant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: emotionalToneOfScenes Context triple: [Dumbo's mother, emotionalToneOfScenes, poignant]
-
A.
emotionalToneOfScene
chosen
Indicates the prevailing emotional quality or mood conveyed by a particular scene.
-
B.
emotionalScope
Indicates the range or extent of emotions involved or affected within a given relationship or situation.
-
C.
emotionalDynamic
Indicates how emotions, moods, or affective states change, interact, or influence each other between entities over time.
-
D.
hasEmotionalDimension
Indicates that something involves, expresses, or affects emotions as a significant aspect of its nature or impact.
-
E.
emotionValence
Indicates the positive, negative, or neutral emotional value or tone associated with an entity, event, or state.
- 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_69f76e3a94fc81908edc175e8d259e80 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fcc7a4d7f881908b43b960911b81e9 |
completed | May 7, 2026, 5:11 p.m. |
| PD | Predicate disambiguation | batch_69fcc589720c819089c8f500fea3c86a |
completed | May 7, 2026, 5:02 p.m. |
Created at: May 3, 2026, 4:31 p.m.