Triple
T4026444
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rain Man (1988 film) score |
E83600
|
entity |
| Predicate | tempoCharacteristics |
P38111
|
FINISHED |
| Object | predominantly mid-tempo |
—
|
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: predominantly mid-tempo | Statement: [Rain Man (1988 film) score, tempoCharacteristics, predominantly mid-tempo]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tempoCharacteristics Context triple: [Rain Man (1988 film) score, tempoCharacteristics, predominantly mid-tempo]
-
A.
tempoFeature
chosen
Indicates a relationship where a musical or rhythmic element is characterized by, or associated with, a specific tempo-related property or feature.
-
B.
tempoCharacter
Indicates the characteristic speed or pacing quality associated with an action, event, or process.
-
C.
tempo
Indicates the speed or pace at which an action, process, or sequence unfolds over time.
-
D.
trainingCharacteristic
Indicates that an entity has a specific property, feature, or quality related to training (such as method, intensity, or style).
-
E.
notableSongCharacteristic
Indicates that a song is distinguished by a particular notable feature or quality, such as style, structure, or performance trait.
- 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_69aed92e29ac819080f7a98b594fec05 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefaed37e48190844032e7e77163e0 |
completed | March 9, 2026, 4:53 p.m. |
| PD | Predicate disambiguation | batch_69aef8fc78ec819092d4dab88d85a141 |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:36 p.m.