Triple
T37712285
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zorro’s Fighting Legion |
E939371
|
entity |
| Predicate | hasFilmColorProcess |
P13343
|
FINISHED |
| Object | black and white |
—
|
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: black and white | Statement: [Zorro’s Fighting Legion, hasFilmColorProcess, black and white]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFilmColorProcess Context triple: [Zorro’s Fighting Legion, hasFilmColorProcess, black and white]
-
A.
hasFilmColorType
chosen
Indicates that a film is associated with a particular color process or color classification (e.g., color, black-and-white).
-
B.
hasPhotographicProcess
Indicates that something is associated with, created by, or characterized through a specific photographic process or technique.
-
C.
hasColorProcess
Indicates a relationship where an entity is associated with a specific method or process used to apply, change, or manage its color.
-
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_69f76edb49dc8190b951dce9ce6ef789 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a007338002081908cb6340d65ff86d5 |
completed | May 10, 2026, 11:59 a.m. |
| PD | Predicate disambiguation | batch_6a0072a137ac8190a7debeb28e738e03 |
completed | May 10, 2026, 11:57 a.m. |
Created at: May 3, 2026, 4:18 p.m.