Triple
T13071290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La La Land (film score) |
E329460
|
entity |
| Predicate | themeCharacteristic |
P108297
|
FINISHED |
| Object | recurring leitmotifs |
—
|
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 leitmotifs | Statement: [La La Land (film score), themeCharacteristic, recurring leitmotifs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: themeCharacteristic Context triple: [La La Land (film score), themeCharacteristic, recurring leitmotifs]
-
A.
catalogCharacteristic
Indicates that a catalog has a specific characteristic or attribute associated with it.
-
B.
dataCharacteristic
Indicates that one entity specifies a property, attribute, or feature that characterizes a given piece of data.
-
C.
characterTheme
Indicates that a particular theme, motif, or conceptual focus is associated with a given character.
-
D.
thematicCharacter
Indicates that an entity serves as a central or recurring figure embodying key themes or motifs within a narrative or discourse.
-
E.
rulesCharacteristic
Indicates that one entity establishes or defines a characteristic, property, or feature that another entity must follow or conform to.
- 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_69d80771749c81909a6d9197b9504872 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d980ee6130819095d835e7ff6a8c5b |
completed | April 10, 2026, 10:59 p.m. |
| PD | Predicate disambiguation | batch_69d9803d46688190bac6b7d208f08d01 |
completed | April 10, 2026, 10:57 p.m. |
| PDg | Predicate description generation | batch_69d980e622e8819087a69bfb1660dd64 |
completed | April 10, 2026, 10:59 p.m. |
Created at: April 9, 2026, 9 p.m.