Triple
T28972535
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Digital Pictures |
E734311
|
entity |
| Predicate | usedActors |
P187038
|
FINISHED |
| Object | professional film and TV actors in games |
—
|
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: professional film and TV actors in games | Statement: [Digital Pictures, usedActors, professional film and TV actors in games]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedActors Context triple: [Digital Pictures, usedActors, professional film and TV actors in games]
-
A.
oftenPlayedBy
Indicates that one entity frequently performs, portrays, or executes another entity, such as a role, character, or piece of music.
-
B.
usedBoyActors
Indicates that boy actors were employed or cast for a particular role, production, or performance.
-
C.
originalCast
Indicates that the subject is a member of the initial group of performers or participants who first originated a role or production.
-
D.
numberOfActors
Indicates the total count of actors associated with a given entity or context.
-
E.
majorActor
Indicates that the subject is a primary or leading participant in the action, event, or production involving the object.
- F. None of above. chosen
Provenance (4 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_69f05b0d1e7c819092baab93d3fe277e |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69fb3425666081908916fcbf3b5dd907 |
completed | May 6, 2026, 12:29 p.m. |
| PD | Predicate disambiguation | batch_69fb2f5f3164819099429c2cc3d24e01 |
completed | May 6, 2026, 12:09 p.m. |
| PDg | Predicate description generation | batch_69fb3424724c8190ba55ecf66fa0b171 |
completed | May 6, 2026, 12:29 p.m. |
Created at: April 28, 2026, 9:06 a.m.