Triple
T30852924
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grant Withers |
E785832
|
entity |
| Predicate | wasCastInTypicalRole |
P11865
|
FINISHED |
| Object | rugged supporting roles |
—
|
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: rugged supporting roles | Statement: [Grant Withers, wasCastInTypicalRole, rugged supporting roles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wasCastInTypicalRole Context triple: [Grant Withers, wasCastInTypicalRole, rugged supporting roles]
-
A.
typicalCasting
chosen
Indicates that one entity is the usual or standard casting choice for portraying another entity (such as a role, character, or type).
-
B.
typicalCast
Indicates that the associated entities form the usual or characteristic cast of characters commonly appearing in a given work or type of work.
-
C.
playedEarlyRoleIn
Indicates that one entity contributed significantly to the initial or formative stages of another entity’s development, success, or emergence.
-
D.
hasFictionalRole
Indicates that an entity plays or is assigned a specific role within a fictional work or narrative.
-
E.
hasPlayedRole
Indicates that an entity has performed or portrayed a particular role or character in some context (such as a film, play, or production).
- 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_69f224b91c14819084e764832fe67a57 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f6917d9a908190b43d8ce6b17eb8ac |
completed | May 3, 2026, 12:06 a.m. |
| PD | Predicate disambiguation | batch_69f68b7d2794819092fef8a63f4f3de8 |
completed | May 2, 2026, 11:40 p.m. |
Created at: April 29, 2026, 8:46 p.m.