Triple
T12602568
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | America's Oldest Teenager |
E300893
|
entity |
| Predicate | referentPrimaryOccupation |
P93070
|
FINISHED |
| Object | television host |
—
|
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: television host | Statement: [America's Oldest Teenager, referentPrimaryOccupation, television host]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: referentPrimaryOccupation Context triple: [America's Oldest Teenager, referentPrimaryOccupation, television host]
-
A.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
B.
recipientOccupation
Indicates that the object specifies the job, profession, or role held by the recipient in the described relationship or event.
-
C.
mainOccupationFrom
chosen
Indicates that the specified occupation is the primary or main job held by the given entity.
-
D.
occupationType
Indicates the specific kind or category of work, profession, or role that an entity performs or holds.
-
E.
occupationDuringAlias
Indicates that an entity held a particular occupation specifically during the time period when it was known by a given alias.
- 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_69d7bdea2ca881908f379526c13b1145 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9559458dc8190bc4d6e697e99d70e |
completed | April 10, 2026, 7:55 p.m. |
| PD | Predicate disambiguation | batch_69d9541894fc8190a0c3706a414279f0 |
completed | April 10, 2026, 7:48 p.m. |
Created at: April 9, 2026, 5:09 p.m.