Triple
T9326972
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Victor Moore as Pop Cardetti |
E224411
|
entity |
| Predicate | filmFormatOfWork |
P24651
|
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: [Victor Moore as Pop Cardetti, filmFormatOfWork, black-and-white]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmFormatOfWork Context triple: [Victor Moore as Pop Cardetti, filmFormatOfWork, black-and-white]
-
A.
filmMedium
chosen
Indicates the physical or technical format (such as film stock, digital, or video) in which a film is recorded or presented.
-
B.
filmType
Indicates the specific category or genre that a film belongs to.
-
C.
usesFilmFormat
Indicates that one entity employs or is recorded in a particular film format associated with the other entity.
-
D.
filmTypeContext
Indicates the contextual relationship between a film and its type or category within a specific classification or usage setting.
-
E.
filmSetting
Indicates the place, time, or environment in which the events of a film are set or take place.
- 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_69ca8427a0c08190b749831d5ea98f02 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd37aa78648190b786b50402b15569 |
completed | April 1, 2026, 3:20 p.m. |
| PD | Predicate disambiguation | batch_69cc7a643924819097f01144734901cf |
completed | April 1, 2026, 1:52 a.m. |
Created at: March 30, 2026, 7:39 p.m.