Triple
T10739849
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | To Be or Not to Be |
E253291
|
entity |
| Predicate | CaroleLombardFinalFilm |
P95736
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [To Be or Not to Be, CaroleLombardFinalFilm, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: CaroleLombardFinalFilm Context triple: [To Be or Not to Be, CaroleLombardFinalFilm, true]
-
A.
end time of marriage with Humphrey Bogart
Indicates the date and time at which a person’s marriage to Humphrey Bogart ended.
-
B.
hasJoanFontaineRole
Indicates that an entity has a role played by Joan Fontaine in a film, television, or theatrical production.
-
C.
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.
-
D.
hasElizabethTaylorRole
Indicates that an entity has a role that was originally played by, associated with, or famously portrayed by Elizabeth Taylor.
-
E.
barbaraStanwyckRole
Indicates that the subject is a role or character portrayed by Barbara Stanwyck.
- 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_69d6aa5e51e8819095f06881cecf152e |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d71043106c819091939950f532eda5 |
completed | April 9, 2026, 2:34 a.m. |
| PD | Predicate disambiguation | batch_69d6f30df9948190ab3cdc33977fac14 |
completed | April 9, 2026, 12:30 a.m. |
| PDg | Predicate description generation | batch_69d6fa323564819097b207eb53f8a9b8 |
completed | April 9, 2026, 1 a.m. |
Created at: April 8, 2026, 9:14 p.m.