Triple
T19333951
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princess Leia’s Theme |
E483569
|
entity |
| Predicate | hasMotivicFunction |
P135456
|
FINISHED |
| Object | character leitmotif |
—
|
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: character leitmotif | Statement: [Princess Leia’s Theme, hasMotivicFunction, character leitmotif]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMotivicFunction Context triple: [Princess Leia’s Theme, hasMotivicFunction, character leitmotif]
-
A.
hasMonodromy
Indicates that one mathematical object exhibits a monodromy action or structure with respect to another (typically along loops in a parameter or base space).
-
B.
hasMythicFunction
Indicates that something serves a symbolic, narrative, or ritual role within a mythic or mythological framework.
-
C.
usesMotifsFrom
Indicates that one entity incorporates or draws upon recurring themes, patterns, or elements that originate from another entity.
-
D.
hasHodgeNumbers
Indicates that an entity (typically a geometric or algebraic object) is associated with specific Hodge numbers describing its Hodge decomposition or Hodge structure.
-
E.
hasMoralFunction
Indicates that an entity serves or fulfills a role related to moral or ethical considerations.
- F. None of above. chosen
Provenance (4 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_69d8e8d13e3c81909d91d1d5ec37c095 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e61643f7b8819088a716e54a579afa |
completed | April 20, 2026, 12:04 p.m. |
| PD | Predicate disambiguation | batch_69e4dd12303c8190a2027c062b2dff40 |
completed | April 19, 2026, 1:48 p.m. |
| PDg | Predicate description generation | batch_69e4df51ac6c819091ce72b07790ffa6 |
completed | April 19, 2026, 1:57 p.m. |
Created at: April 10, 2026, 1:33 p.m.