Triple
T20521890
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Russell A. Marvin |
E503829
|
entity |
| Predicate | appearsInColorOrBW |
P124117
|
FINISHED |
| Object | black-and-white film |
—
|
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 film | Statement: [Dr. Russell A. Marvin, appearsInColorOrBW, black-and-white film]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appearsInColorOrBW Context triple: [Dr. Russell A. Marvin, appearsInColorOrBW, black-and-white film]
-
A.
appearsIn
Indicates that an entity is present, featured, or occurs within a particular context, work, or medium.
-
B.
containsColor
Indicates that one entity includes or exhibits the color specified by another entity.
-
C.
isColor
Indicates that one entity represents the color attribute or hue of another entity.
-
D.
playedInBlackAndWhiteOrColorFilm
chosen
Indicates that the subject participated in a film, regardless of whether it was produced in black-and-white or in color.
-
E.
isInColor
Indicates that one entity exists, appears, or is represented using a particular color specified by the other 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_69e0b4b3a6e08190ae663701f50fab8e |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e69f46488c819093687b4e07837793 |
completed | April 20, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69e59fdb7ad88190924176c32a195db3 |
completed | April 20, 2026, 3:39 a.m. |
Created at: April 16, 2026, 11:36 a.m.