Triple
T3884072
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bus Stop |
E92895
|
entity |
| Predicate | MarilynMonroeRoleType |
P51616
|
FINISHED |
| Object | saloon singer |
—
|
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: saloon singer | Statement: [Bus Stop, MarilynMonroeRoleType, saloon singer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: MarilynMonroeRoleType Context triple: [Bus Stop, MarilynMonroeRoleType, saloon singer]
-
A.
leadActress
Indicates that the subject is the primary female performer in the specified film, show, or production.
-
B.
isMarilyn
Indicates that the subject is (or is being identified as) Marilyn.
-
C.
madeFamousByFilm
Indicates that something became widely known or gained significant public recognition as a result of being featured in a film.
-
D.
hasGingerRogersRole
Indicates that an entity is assigned or associated with a role specifically identified as the "Ginger Rogers" role in a given context or production.
-
E.
supportingActorAwardRecipient
Indicates that an entity has received an award specifically for a supporting acting role in a performance or production.
- 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_69aed9697de0819087c2559295ff3d12 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aeec9029908190a7b36a3827734db1 |
completed | March 9, 2026, 3:51 p.m. |
| PD | Predicate disambiguation | batch_69aee759609c8190985e96ec6d96dedd |
completed | March 9, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69aee80858a481909961a33fb50ff8d1 |
completed | March 9, 2026, 3:32 p.m. |
Created at: March 9, 2026, 3:20 p.m.