Triple
T30988486
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Love Nest |
E789597
|
entity |
| Predicate | hasMarilynMonroeRoleType |
P51616
|
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: [Love Nest, hasMarilynMonroeRoleType, supporting role]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMarilynMonroeRoleType Context triple: [Love Nest, hasMarilynMonroeRoleType, supporting role]
-
A.
MarilynMonroeRoleType
chosen
Indicates the type or category of role associated with Marilyn Monroe in a given context.
-
B.
MarilynMonroeRole
Indicates that the subject entity has (or had) a role or character portrayed by Marilyn Monroe.
-
C.
hasMarleneDietrichRoleType
Indicates that an entity has a specific type or category of role associated with Marlene Dietrich.
-
D.
hasRitaHayworthRole
Indicates that an entity has a role or character associated with Rita Hayworth, such as portraying her or a role closely linked to her.
-
E.
hasMarleneDietrichRole
Indicates that an entity has a role or character specifically associated with Marlene Dietrich, such as portraying her or a role closely linked to her persona.
- 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_69f224c550b081909ddfceb0c3d03bdd |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69ffed8912488190baa05f572e5b1b89 |
completed | May 10, 2026, 2:29 a.m. |
| PD | Predicate disambiguation | batch_69ffed12a76c8190ad85c6ac869c72e9 |
completed | May 10, 2026, 2:27 a.m. |
Created at: April 29, 2026, 8:56 p.m.