Triple
T27051621
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cheyenne’s Theme |
E684786
|
entity |
| Predicate | hasRecurrentUse |
P47229
|
FINISHED |
| Object | recurring leitmotif in film |
—
|
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: recurring leitmotif in film | Statement: [Cheyenne’s Theme, hasRecurrentUse, recurring leitmotif in film]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRecurrentUse Context triple: [Cheyenne’s Theme, hasRecurrentUse, recurring leitmotif in film]
-
A.
hasNumberOfUses
Indicates the quantity of times something can be or is intended to be used.
-
B.
usesRepetition
chosen
Indicates that one entity employs repeated elements, actions, or patterns as a deliberate feature or technique in relation to another entity or context.
-
C.
continuouslyUsedAs
Indicates that one entity is persistently and repeatedly employed or utilized as another entity or for a particular function over an extended, uninterrupted period.
-
D.
hasDailyUse
Indicates that something is used or occurs on a daily, regular basis.
-
E.
enablesRepeatedUse
Indicates that one entity provides the capability or conditions for another entity to be used multiple times without needing replacement or reset.
- 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_69ef14829fac8190914bef9ecc3005d7 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69fe68a4b67881909ca1d9f276f922e0 |
completed | May 8, 2026, 10:50 p.m. |
| PD | Predicate disambiguation | batch_69fe680234c88190b01f953987b74972 |
completed | May 8, 2026, 10:47 p.m. |
Created at: April 27, 2026, 8:14 a.m.