Triple
T19045063
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Captain America: The Winter Soldier (2014) film score |
E466109
|
entity |
| Predicate | basedOnTone |
P7344
|
FINISHED |
| Object | dark |
—
|
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: dark | Statement: [Captain America: The Winter Soldier (2014) film score, basedOnTone, dark]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: basedOnTone Context triple: [Captain America: The Winter Soldier (2014) film score, basedOnTone, dark]
-
A.
contributesToTone
Indicates that one entity plays a role in shaping, influencing, or determining the overall tone or mood of another entity.
-
B.
inTonality
Indicates that something (such as a musical element, passage, or piece) is expressed, structured, or interpreted within a specific musical key or tonal framework.
-
C.
isToneNeutral
Indicates that the tone of the referenced content is neither positive nor negative, but emotionally neutral or unbiased.
-
D.
tone
chosen
Indicates the characteristic attitude or emotional quality expressed in how something is communicated or presented.
-
E.
hasTonalityShift
Indicates a change in the tonal quality, mood, or key within a piece or segment, marking a shift from one tonality to another.
- 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_69d8dd0359648190bc2a9202c5cf29d2 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d803b2b08190b057d4b5bc555d4f |
completed | April 20, 2026, 7:38 a.m. |
| PD | Predicate disambiguation | batch_69e4b99633c8819097988608c278ecf8 |
completed | April 19, 2026, 11:16 a.m. |
Created at: April 10, 2026, 12:03 p.m.