Triple
T36793099
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sebastian Wilder in a traffic jam |
E909106
|
entity |
| Predicate | characterProfessionShown |
P153983
|
FINISHED |
| Object | aspiringJazzMusician |
—
|
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: aspiringJazzMusician | Statement: [Sebastian Wilder in a traffic jam, characterProfessionShown, aspiringJazzMusician]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterProfessionShown Context triple: [Sebastian Wilder in a traffic jam, characterProfessionShown, aspiringJazzMusician]
-
A.
portrayedProfessionOfCharacter
chosen
Indicates that one entity is the profession or occupation depicted as being held by a particular character.
-
B.
portrayedByProfession
Indicates that an entity is depicted or represented by someone acting in a specified professional capacity.
-
C.
portraysProfession
Indicates that one entity depicts or represents another entity in a specific profession or occupational role.
-
D.
featuresProtagonistOccupation
Indicates that the work’s main character has a specified occupation or job role.
-
E.
followsCharacterProfession
Indicates that one character’s professional role or occupation comes after or is modeled on another character’s profession.
- 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_69f76e7a937c81909ed7359641e670f6 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69ffc3f0b19c8190b5a749bc3cad21dd |
completed | May 9, 2026, 11:32 p.m. |
| PD | Predicate disambiguation | batch_69ffc1b882808190932b2d43ea5537c9 |
completed | May 9, 2026, 11:22 p.m. |
Created at: May 3, 2026, 4:12 p.m.