Triple
T4556456
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Live and Learn |
E120490
|
entity |
| Predicate | centralCharacterOccupation |
P21567
|
FINISHED |
| Object | history professor |
—
|
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: history professor | Statement: [Live and Learn, centralCharacterOccupation, history professor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: centralCharacterOccupation Context triple: [Live and Learn, centralCharacterOccupation, history professor]
-
A.
featuresProtagonistOccupation
chosen
Indicates that the work’s main character has a specified occupation or job role.
-
B.
followsCharacterOccupation
Indicates that one character’s occupation or job role comes after or succeeds another character’s occupation in a sequence or progression.
-
C.
notableCharacterOccupation
Indicates that a notable character is associated with a specific occupation or professional role.
-
D.
sonOccupation
Indicates that a specified occupation is the job or professional role held by a person's son.
-
E.
fictionalOccupation
Indicates that one entity is the imaginary or narrative-based job, role, or profession attributed to another entity within a fictional context.
- 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_69bd4636f1648190a701445c2fcd9c17 |
completed | March 20, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69bd5814f56c8190a65f61f6148b7e5a |
completed | March 20, 2026, 2:22 p.m. |
| PD | Predicate disambiguation | batch_69bd5223423c81908317351b58cff5f5 |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 1:09 p.m.