Triple
T28585052
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barbara Stanwyck as Lee Leander |
E723474
|
entity |
| Predicate | characterOccupationOrRole |
P153983
|
FINISHED |
| Object | shoplifter |
—
|
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: shoplifter | Statement: [Barbara Stanwyck as Lee Leander, characterOccupationOrRole, shoplifter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterOccupationOrRole Context triple: [Barbara Stanwyck as Lee Leander, characterOccupationOrRole, shoplifter]
-
A.
roleOfPerson
Indicates that a specific role, function, or position is assigned to or held by a particular person.
-
B.
settingOfCharacterOccupation
Indicates the place or environment in which a character performs or holds their occupation.
-
C.
portrayedProfessionOfCharacter
chosen
Indicates that one entity is the profession or occupation depicted as being held by a particular character.
-
D.
notableCharacterOccupation
Indicates that a notable character is associated with a specific occupation or professional role.
-
E.
otherProtagonistOccupation
Indicates that another main character in the narrative has a specific occupation or job role.
- 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_69f01d7f92e481909847f5f3f3174a89 |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69fbad1e94988190b86d447a68e65067 |
completed | May 6, 2026, 9:05 p.m. |
| PD | Predicate disambiguation | batch_69fba881b8e0819094790935152b99a1 |
completed | May 6, 2026, 8:45 p.m. |
Created at: April 28, 2026, 4:17 a.m.