Triple
T2351000
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 28 Weeks Later (cameo)? |
E47446
|
entity |
| Predicate | associatedActorPreviousRole |
P6709
|
FINISHED |
| Object | Jim in 28 Days Later |
—
|
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: Jim in 28 Days Later | Statement: [28 Weeks Later (cameo)?, associatedActorPreviousRole, Jim in 28 Days Later]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedActorPreviousRole Context triple: [28 Weeks Later (cameo)?, associatedActorPreviousRole, Jim in 28 Days Later]
-
A.
hasPlayedRole
Indicates that an entity has performed or portrayed a particular role or character in some context (such as a film, play, or production).
-
B.
playedRoleIn
Indicates that an entity performed or assumed a specific role or character within a particular event, production, or context.
-
C.
portrayedByAlsoPlays
Indicates that the actor who portrays a given character also plays another specified role or character.
-
D.
actingRoleType
Indicates the specific type or category of role an entity performs when acting in a particular capacity or function.
-
E.
followedByRoleInCareerOf
chosen
Indicates that one role or position directly succeeds another in the sequence of roles within a single entity’s career.
- 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_69a88a1b678c8190bce986922ba60ce0 |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abcb802da08190980100444010f91e |
completed | March 7, 2026, 6:53 a.m. |
| PD | Predicate disambiguation | batch_69abc5981ce48190a3f7852d28276e11 |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 4, 2026, 7:54 p.m.