Triple
T27122371
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Going Highbrow |
E687033
|
entity |
| Predicate | mainCharacterLaterOccupation |
P104743
|
FINISHED |
| Object | radio star |
—
|
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: radio star | Statement: [Going Highbrow, mainCharacterLaterOccupation, radio star]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainCharacterLaterOccupation Context triple: [Going Highbrow, mainCharacterLaterOccupation, radio star]
-
A.
laterOccupationInFiction
chosen
Indicates that a fictional character holds a particular occupation at a later point in the narrative or timeline, distinct from their earlier roles.
-
B.
otherProtagonistOccupation
Indicates that another main character in the narrative has a specific occupation or job role.
-
C.
characterFormerOccupation
Indicates that a character previously held a specific occupation but no longer does.
-
D.
hasOccupationDuringStory
Indicates that an entity holds or performs a particular occupation or job role during the time span covered by the story.
-
E.
featuresProtagonistOccupation
Indicates that the work’s main character has a specified 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_69ef148c2b588190afc15b529f7af845 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69f6b2a65c7c8190ac40f1466ceadefc |
completed | May 3, 2026, 2:27 a.m. |
| PD | Predicate disambiguation | batch_69f6b14d7d508190bc7d4c89dfba4a32 |
completed | May 3, 2026, 2:22 a.m. |
Created at: April 27, 2026, 9 a.m.