Triple
T5473809
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Blue Dahlia |
E122898
|
entity |
| Predicate | hasFilmNoirElements |
P41012
|
FINISHED |
| Object | hardboiled dialogue |
—
|
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: hardboiled dialogue | Statement: [The Blue Dahlia, hasFilmNoirElements, hardboiled dialogue]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFilmNoirElements Context triple: [The Blue Dahlia, hasFilmNoirElements, hardboiled dialogue]
-
A.
hasFilmStyle
chosen
Indicates that a film exhibits or is characterized by a particular cinematic style or aesthetic approach.
-
B.
hasDramaticElements
Indicates that something contains features or qualities characteristic of drama, such as heightened emotion, tension, or conflict.
-
C.
hasCinematicThemes
Indicates that something incorporates or is characterized by themes, motifs, or stylistic elements commonly associated with cinema or film.
-
D.
hasComedyElements
Indicates that something contains humorous or comedic aspects as part of its overall content or style.
-
E.
hasEnigmaticCharacter
Indicates that something possesses a mysterious, puzzling, or difficult-to-interpret quality or nature.
- 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_69bd46459ff48190823377457bcf7128 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd9232ded08190b4142e604319b2ba |
completed | March 20, 2026, 6:30 p.m. |
| PD | Predicate disambiguation | batch_69bd91a58c448190904964a439045e05 |
completed | March 20, 2026, 6:27 p.m. |
Created at: March 20, 2026, 2:09 p.m.