Triple
T5528128
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Score for "A Star Is Born" (1937) |
E144975
|
entity |
| Predicate | temporalSetting |
P20835
|
FINISHED |
| Object | 1930s Hollywood film music |
—
|
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: 1930s Hollywood film music | Statement: [Score for "A Star Is Born" (1937), temporalSetting, 1930s Hollywood film music]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: temporalSetting Context triple: [Score for "A Star Is Born" (1937), temporalSetting, 1930s Hollywood film music]
-
A.
temporalEffect
Indicates a relationship where one event, state, or action produces consequences or changes that occur at a later time.
-
B.
temporality
Indicates the time-related relationship between events or states, such as their order, duration, or simultaneity.
-
C.
temporalAspect
Indicates the time-related characteristics or phase (such as duration, frequency, or temporal status) associated with an event or relationship.
-
D.
timeOfSetting
chosen
Indicates the specific time at which an event, object, or phenomenon is set, scheduled, or takes place.
-
E.
hasTemporalUse
Indicates that something is used, applicable, or valid only during a specific time or temporal interval.
- 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_69c008f873a481909b4d9f7e2db3c37d |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f8b6c348190b7d414dc1907d09a |
completed | March 22, 2026, 4:57 p.m. |
| PD | Predicate disambiguation | batch_69c01b0a06348190b39ac9fe80d2836a |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:34 p.m.