Triple
T21160139
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moonraker (film) score |
E521416
|
entity |
| Predicate | includesRomanticCues |
P143103
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Moonraker (film) score, includesRomanticCues, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesRomanticCues Context triple: [Moonraker (film) score, includesRomanticCues, yes]
-
A.
hasRomanticSceneAt
Indicates that a romantic scene occurs at a specific location or point in time within a work or context.
-
B.
romanticLeadIn
Indicates that one entity is the primary romantic interest or central romantic partner of another within a narrative or context.
-
C.
romanticFeeling
Indicates that one entity experiences romantic attraction or affection toward another entity.
-
D.
romanticPattern
Indicates a recurring style, tendency, or structure in how romantic relationships or attractions develop or are expressed between entities.
-
E.
encouragesRomanceBetween
Indicates that one entity promotes, supports, or fosters a romantic relationship between two other entities.
- 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_69e0b50d1ea481909c07e63c3ead9316 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7252f70888190b8e6109cc4099ecc |
completed | April 21, 2026, 7:20 a.m. |
| PD | Predicate disambiguation | batch_69e5f5f8a5bc819081918c7fa8e4496d |
completed | April 20, 2026, 9:46 a.m. |
| PDg | Predicate description generation | batch_69e5f993240c8190847c0b08e65726c8 |
completed | April 20, 2026, 10:01 a.m. |
Created at: April 16, 2026, 2:59 p.m.