Triple
T5417463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Delicious |
E121165
|
entity |
| Predicate | hasSheilaHancockRole |
P36975
|
FINISHED |
| Object | supporting role |
—
|
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: supporting role | Statement: [Delicious, hasSheilaHancockRole, supporting role]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSheilaHancockRole Context triple: [Delicious, hasSheilaHancockRole, supporting role]
-
A.
hasMainRole
Indicates that an entity holds the primary or most significant role in relation to another entity or context.
-
B.
hasPlayedRole
chosen
Indicates that an entity has performed or portrayed a particular role or character in some context (such as a film, play, or production).
-
C.
playedRoleIn
Indicates that an entity performed or assumed a specific role or character within a particular event, production, or context.
-
D.
hasFictionalRole
Indicates that an entity plays or is assigned a specific role within a fictional work or narrative.
-
E.
hasCrewRole
Indicates that an entity serves in a specific role or position within a crew associated with another 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_69bd463a41cc8190b32ff5af2b96ca93 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd87e620f081909eb9a5e1f284e5a2 |
completed | March 20, 2026, 5:46 p.m. |
| PD | Predicate disambiguation | batch_69bd8469f5e48190bbe5c8bdfe8925ea |
completed | March 20, 2026, 5:31 p.m. |
Created at: March 20, 2026, 2:05 p.m.