Triple
T38238006
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ed Wynn as Albert Dussell |
E1013675
|
entity |
| Predicate | sourceCharacterRealPerson |
P86334
|
FINISHED |
| Object | Fritz Pfeffer |
—
|
NE NERFINISHED |
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: Fritz Pfeffer | Statement: [Ed Wynn as Albert Dussell, sourceCharacterRealPerson, Fritz Pfeffer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sourceCharacterRealPerson Context triple: [Ed Wynn as Albert Dussell, sourceCharacterRealPerson, Fritz Pfeffer]
-
A.
realPerson
Indicates that the referenced entity corresponds to an actual human individual, as opposed to a fictional, anonymous, or non-human entity.
-
B.
basedOnRealPersonFor
Indicates that one entity is created, modeled, or inspired using a specific real person as its basis.
-
C.
characterRealWorldCounterpart
chosen
Indicates that a fictional character is based on, inspired by, or directly corresponds to a specific real-world person.
-
D.
realPersonDepicted
Indicates that a real, actual person (not fictional or generic) is visually represented or shown in the subject entity.
-
E.
featuresRealPersonAsHimself
Indicates that a real person appears in the work portraying themself rather than a fictional character.
- 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_69f76dd72a248190a5fe18db2bd1eb15 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fcec5f8b448190b48330a19b462d24 |
completed | May 7, 2026, 7:47 p.m. |
| PD | Predicate disambiguation | batch_69fceaf1e23881908ca24160a638e329 |
completed | May 7, 2026, 7:41 p.m. |
Created at: May 3, 2026, 4:30 p.m.