Triple
T23209853
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Odd Jobs |
E580559
|
entity |
| Predicate | hasJuliannePhillipsRoleType |
P151368
|
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: [Odd Jobs, hasJuliannePhillipsRoleType, supporting role]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasJuliannePhillipsRoleType Context triple: [Odd Jobs, hasJuliannePhillipsRoleType, supporting role]
-
A.
hasKhloeRole
Indicates that an entity holds or is assigned the specific role identified as "Khloe" in relation to another entity or context.
-
B.
hasShirleyTempleRoleType
Indicates that an entity has a specific type or category of role related to Shirley Temple.
-
C.
hasJoanFontaineRole
Indicates that an entity has a role played by Joan Fontaine in a film, television, or theatrical production.
-
D.
hasElizabethTaylorRole
Indicates that an entity has a role that was originally played by, associated with, or famously portrayed by Elizabeth Taylor.
-
E.
hasMarleneDietrichRoleType
Indicates that an entity has a specific type or category of role associated with Marlene Dietrich.
- 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_69e24602ae1481908aaa6bc7ca493867 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f191609c64819096ace0d286d36f76 |
completed | April 29, 2026, 5:04 a.m. |
| PD | Predicate disambiguation | batch_69effcccee508190a7ae311fdd319806 |
completed | April 28, 2026, 12:18 a.m. |
| PDg | Predicate description generation | batch_69f01d8770d081908897c28b04e5faea |
completed | April 28, 2026, 2:37 a.m. |
Created at: April 17, 2026, 4:07 p.m.