Triple
T19388986
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dorothy Michaels |
E485010
|
entity |
| Predicate | portrayalInvolves |
P100368
|
FINISHED |
| Object | male actor in female 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: male actor in female role | Statement: [Dorothy Michaels, portrayalInvolves, male actor in female role]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayalInvolves Context triple: [Dorothy Michaels, portrayalInvolves, male actor in female role]
-
A.
portrayalFeature
Indicates that one entity serves as a characteristic, aspect, or attribute highlighted in the depiction or representation of another entity.
-
B.
portrayalLedTo
Indicates that one entity’s portrayal of another caused or significantly contributed to a subsequent outcome, reaction, or state involving that other entity.
-
C.
portrayalRecognition
Indicates that one entity recognizes or identifies another entity as a portrayal or representation of a particular subject or character.
-
D.
portraysPersonAs
chosen
Indicates that one entity represents, depicts, or characterizes another person in a particular way or role.
-
E.
portrayalIntroduced
Indicates that one entity is introduced or presented as a portrayal or depiction of another entity.
- 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_69d8e8d460d88190abf0591c5c9d2b0c |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e61b425e848190ab5ae8ae0a034fe2 |
completed | April 20, 2026, 12:25 p.m. |
| PD | Predicate disambiguation | batch_69e4fd602f008190aa9bc76ae17e4ce1 |
completed | April 19, 2026, 4:05 p.m. |
Created at: April 10, 2026, 1:36 p.m.