Triple
T17018154
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lola |
E412873
|
entity |
| Predicate | hasFilmColor |
P13343
|
FINISHED |
| Object | color |
—
|
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: color | Statement: [Lola, hasFilmColor, color]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFilmColor Context triple: [Lola, hasFilmColor, color]
-
A.
hasFilmColorType
chosen
Indicates that a film is associated with a particular color process or color classification (e.g., color, black-and-white).
-
B.
playedInBlackAndWhiteOrColorFilm
Indicates that the subject participated in a film, regardless of whether it was produced in black-and-white or in color.
-
C.
hasFilmStyle
Indicates that a film exhibits or is characterized by a particular cinematic style or aesthetic approach.
-
D.
supportsColorSampling
Indicates that one entity can perform or accommodate color sampling operations on another entity or its data.
-
E.
hasBetterColorReproductionThan
Indicates that one entity produces more accurate or higher-quality color representation than another entity.
- 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_69d886cc4170819093deddc7b8b4b6a7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d480a58c8190a3912d26debb4311 |
completed | April 18, 2026, 6:59 p.m. |
| PD | Predicate disambiguation | batch_69e35d5be7f48190af9db67a1e23850f |
completed | April 18, 2026, 10:30 a.m. |
Created at: April 10, 2026, 5:33 a.m.